RecSys.md

Recommendation Papers — RecSys

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RecSys 2025

전체 239편 중 179편 (74.9%) — 키워드: recommendation / recommender / recommend

• "Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation

  On music streaming services, listening sessions are often composed of a balance of familiar and new tracks.

• "We Share Our Code Online": Why This Is Not Enough to Ensure Reproducibility and Progress in Recommender Systems Research

  Issues with reproducibility have been identified as a major factor hampering progress in recommender systems research.

• 12th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'25)

  The 12th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’25) takes a user-centric perspective on recommender systems research.

• A Dual-Key Attention Framework for Sequential Recommendation with Side Information

  Sequential recommendation (SR) aims to predict users’ future interactions based on their historical behavior.

• A Hands-on Dive Into Quantum Computing for Recommender Systems

  Quantum Computing (QC) has gained attention for its promise to substantially accelerate the solution of many computationally intensive tasks.

• A Language Model-Based Playlist Generation Recommender System

  The title of a playlist often reflects an intended mood or theme, allowing creators to easily locate their content and enabling other users to discover music that matches specific situations and needs.

• A Media Content Recommendation Method for Playlist Curators using LLM-Based Query Expansion

  Playlist curation is a key factor in media content discovery services, yet finding diverse, relevant content is challenging for curators due to time-consuming manual query crafting.

• A Multi-Factor Collaborative Prediction for Review-based Recommendation

  For items, the higher the click-through rate, the higher the rating.

• A Multistakeholder Approach to Value-Driven Co-Design of Recommender Systems Evaluation Metrics in Digital Archives

  This paper presents the first multistakeholder approach for translating diverse stakeholder values into an evaluation metric setup for Recommender Systems (RecSys) in digital archives.

• A Non-Parametric Choice Model That Learns How Users Choose Between Recommended Options

  Choice models predict which items users choose from presented options.

• A Reproducibility Study of Product-side Fairness in Bundle Recommendation

  Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results.

• A Tutorial on Agentic LLM for Recommender Systems

  Recent breakthroughs in multimodal large language models (MLLMs) have paved the way for developing agentic recommender systems that go beyond traditional recommendation methods.

• A Tutorial on Recent Advances in Generative Conversational Recommender Systems

  Conversational recommender systems (CRSs) are increasingly vital for delivering multi-turn, context-aware recommendations.

• Adding Value to Low-Resource Industrial Recommender Systems

  This research proposes a modular, resource-aware framework for industrial recommender systems that enables the integration and evaluation of stakeholder values at each stage of the recommendation pipeline.

• Addressing Multi-stakeholder Fairness Concerns in Recommender Systems Through Social Choice

  Fairness in recommender systems has been discussed on the group and individual level with concerns for both providers and consumers.

• Advancing User-Centric Evaluation and Design of Conversational Recommender Systems

  Conversational Recommender Systems (CRS) are rapidly evolving with advancements in large language models (LLMs), enabling richer, more adaptive user interactions.

• Affect-aware Cross-Domain Recommendation for Art Therapy via Music Preference Elicitation

  Art Therapy (AT) is an established practice that facilitates emotional processing and recovery through creative expression.

• Are Recommender Systems Serving Children? Toward Child-Aware Design and Evaluation

  Recommender Systems research continuously improves recommendation strategies to meet the needs of a wide range of users and other stakeholders.

• Are We Really Making Recommendations Robust? Revisiting Model Evaluation for Denoising Recommendation

  Implicit feedback data has emerged as a fundamental component of modern recommender systems due to its scalability and availability.

• ArtEx: A User-Controllable Web Interface for Visual Art Recommendations

  We introduce a web-based interface for visual art recommendations, empowering users to adjust popularity and diversity through intuitive sliders.

• Auditing Recommender Systems for User Empowerment in Very Large Online Platforms under the Digital Services Act

  The governance of recommender systems (RSs) in very large online platforms (VLOPs) is expected to change significantly under the Digital Services Act (DSA), which imposes new obligations on transparency and user control.

• Balanced Public Service Media Recommendation Trade-offs with a Light Carbon Footprint

  Public service media (PSM) providers commonly face the challenge of balancing user engagement metrics and public value.

• Balancing Fine-tuning and RAG: A Hybrid Strategy for Dynamic LLM Recommendation Updates

  Large Language Models (LLMs) empower recommendation systems through their advanced reasoning and planning capabilities.

• Bayesian Perspectives on Offline Evaluation for Recommender Systems

  Offline evaluation is a fundamental component in the deployment and development of better recommender systems.

• Beyond Algorithms: Reclaiming the Interdisciplinary Roots of Recommender Systems (BEYOND 2025)

  This workshop challenges the machine learning-centric focus of modern recommender systems research by reconnecting the field with its interdisciplinary origins and exploring the non-algorithmic dimensions that are crucial to effective recommendation.

• Beyond Clicks: Eye-Tracking Insights into User Responses to Different Recommendation Types

  Modern recommender systems increasingly rely on implicit human feedback to enhance recommendation quality, personalization, and user engagement.

• Beyond Immediate Click: Engagement-Aware and MoE-Enhanced Transformers for Sequential Movie Recommendation

  Modern video streaming services heavily rely on recommender systems.

• Beyond Persuasion: Adaptive Warnings and Balanced Explanations for Informed Decision-Making in Recommender Systems

  As recommender systems become deeply embedded in digital platforms, designing explanations that are ethical, effective, and user-centered is increasingly important.

• Beyond Top-1: Addressing Inconsistencies in Evaluating Counterfactual Explanations for Recommender Systems

  Explainability in recommender systems (RS) remains a pivotal yet challenging research frontier.

• Beyond Visit Trajectories: Enhancing POI Recommendation via LLM-Augmented Text and Image Representations

  Recommender systems often rely on user visit trajectories, but the integration and representation of diverse side information remains a key challenge.

• Biases in LLM-Generated Musical Taste Profiles for Recommendation

  One particularly promising use case of Large Language Models (LLMs) for recommendation is the automatic generation of Natural Language (NL) user taste profiles from consumption data.

• Blooming Beats: An Interactive Music Recommender System Grounded in TRACE Principles and Data Humanism

  Music streaming platforms reduce rich listening experiences to algorithmic black boxes, overlooking personal narratives that make music meaningful.

• Breaking Knowledge Boundaries: Cognitive Distillation-enhanced Cross-Behavior Course Recommendation Model

  Online Course Recommendation (CR) stands as a promising educational strategy within online education platforms, with the goal of providing personalized learning experiences for learners and enhancing their learning efficiency.

• Challenges in Perfume Recommender Systems: Navigating Subjectivity, Context and Sensory Data

  Compared to other recommender systems domains, perfume recommendation proves to be highly personalized and more challenging due to the very subjective factors and complex mixture of involved senses.

• Collaborative Interest Modeling in Recommender Systems

  In this paper, we introduce Collaborative Interest Modeling (COIN), a novel approach to tackle interest entanglement and sparse interest representations within multi-interest learning for recommender systems.

• concept2code: Sequential Recommendation with Large Language Models

  Large Language Models (LLMs) have demonstrated remarkable abilities in understanding and generating language, inspiring their adoption in various domains beyond NLP.

• CONSEQUENCES 2025 - The 4th Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

  Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them.

• Consistent Explainers or Unreliable Narrators? Understanding LLM-generated Group Recommendations

  Large Language Models (LLMs) are increasingly being implemented as joint decision-makers and explanation generators for Group Recommender Systems (GRS).

• Context Trails: A Dataset to Study Contextual and Route Recommendation

  Recommender systems in the tourism domain are gaining increasing attention, yet the development of diverse recommendation tasks remains limited, largely due to the scarcity of public datasets.

• Contrastive Conditional Embeddings for Item-based Recommendation at E-commerce Scale

  Item-based recommendation is crucial in e-commerce for helping users navigate the myriad of options available to them.

• Cross-Batch Aggregation for Streaming Learning from Label Proportions in Industrial-Scale Recommendation Systems

  Recent controls over user data have diluted user signals essential to train industrial recommendation systems, replacing traditional event-level labels with aggregated item-level labels.

• D-RDW: Diversity-Driven Random Walks for News Recommender Systems

  This paper introduces Diversity-Driven Random Walks (D-RDW), a lightweight algorithm and re-ranking technique that generates diverse news recommendations.

• Data Access for Recommender Systems Research: leveraging the EU's Digital Services Act

  The European Union (EU) Digital Services Act (DSA) has introduced a novel set of rules for online platforms and search engines, with significant implications for the Recommender Systems community.

• Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based Regularization

  Recommendation models often encode users’ sensitive attributes (e.g., gender or age) in their learned representations during training, leading to biased (e.g., stereotypical) recommendations and potential privacy risks.

• Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest

  The ranking utility function in an ad recommender system, which linearly combines predictions of various business goals, plays a central role in balancing values across the platform, advertisers, and users.

• Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations

  Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders.

• Determinants of Users' Chance-Seeking Behavior in Search-Based Recommendation

  Serendipity has emerged as a promising strategy to counter overspecialization in retrieval and recommendation systems.

• Disentangling User and Item Sequence Patterns in Sequential Recommendation Data Sets

  Sequential recommenders use the ordering of user-item interactions to perform next-item prediction.

• DistillRecDial: A Knowledge-Distilled Dataset Capturing User Diversity in Conversational Recommendation

  Conversational Recommender Systems (CRSs) facilitate item discovery through multi-turn dialogues that elicit user preferences via natural language interaction.

• Do We Really Need Specialization? Evaluating Generalist Text Embeddings for Zero-Shot Recommendation and Search

  Pre-trained language models (PLMs) are widely used to derive semantic representations from item metadata in recommendation and search.

• Don't Get Ahead of Yourself: A Critical Study on Data Leakage in Offline Evaluation of Sequential Recommenders

  While previous studies have investigated data leakage in recommendation, their findings have had little impact on research practice.

• EARL: The 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models

  This article presents our proposal to organize the 2nd Workshop on Evaluating and Applying Recommender Systems with Large Language Models (EARL), to be held in conjunction with the 19th ACM Conference on Recommender Systems (RecSys 2025) in Prague...

• Emotion Vector-Based Fine-Tuning of Large Language Models for Age-Aware Teenage Book Recommendations

  Reading is a vital skill for teenagers as described by the National Institute of Child Health and Human Development, “Reading is the single most important skill necessary for a happy, productive, and successful life." Yet, teens and their parents ...

• End-to-End Time Interval-wise Segmentation for Sequential Recommendation

  Sequential recommendation aims to predict a user’s next interaction based on their historical behavior.

• Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID

  The exponential growth of online content has posed significant challenges to ID-based models in industrial recommendation systems, ranging from extremely high cardinality and dynamically growing ID space, to highly-skewed engagement distributions,...

• Enhancing Online Video Recommendation via a Coarse-to-fine Dynamic Uplift Modeling Framework

  The popularity of short video applications has brought new opportunities and challenges to video recommendation.

• Enhancing Sequential Recommender with Large Language Models for Joint Video and Comment Recommendation

  Nowadays, reading or writing comments on captivating videos has emerged as a critical part of the viewing experience on online video platforms.

• Enhancing Transferability and Consistency in Cross-Domain Recommendations via Supervised Disentanglement

  Cross-domain recommendation (CDR) aims to alleviate the data sparsity by transferring knowledge across domains.

• eSASRec: Enhancing Transformer-based Recommendations in a Modular Fashion

  Since their introduction, Transformer-based models, such as SASRec and BERT4Rec, have become common baselines for sequential recommendations, surpassing earlier neural and non-neural methods.

• Estimating Quantum Execution Requirements for Feature Selection in Recommender Systems Using Extreme Value Theory

  Recent progress in quantum computing has advanced research in quantum-assisted information retrieval and recommender systems, especially for feature selection via Quadratic Unconstrained Binary Optimization (QUBO).

• Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge

  Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bias and online methods such as A/B testing are costly and operational...

• Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation

  Multi-Armed Bandit (MAB) algorithms are widely used in recommender systems that require continuous, incremental learning.

• Exploring the Effect of Context-Awareness and Popularity Calibration on Popularity Bias in POI Recommendations

  Point-of-interest (POI) recommender systems help users discover relevant locations, but their effectiveness is often compromised by popularity bias, which disadvantages less popular, yet potentially meaningful places.

• Exploring the Potential of LLMs for Serendipity Evaluation in Recommender Systems

  Serendipity plays a pivotal role in enhancing user satisfaction within recommender systems, yet its evaluation poses significant challenges due to its inherently subjective nature and conceptual ambiguity.

• FAccTRec 2025: The 8th Workshop on Responsible Recommendation

  The 8th Workshop on Responsible Recommendation (FAccTRec 2025) was held in conjunction with the 19th ACM Conference on Recommender Systems in September, 2025 at Prague, Czech Republic, in a hybrid format.

• Failure Prediction in Conversational Recommendation Systems

  In a Conversational Image Recommendation task, users can provide natural language feedback on a recommended image item, which leads to an improved recommendation in the next turn.

• Fair and Transparent Recommender Systems for Advertisements

  Recommender systems are central to digital platforms, powering content personalization, user engagement, and revenue generation.

• Fashion-AlterEval: A Dataset for Improved Evaluation of Conversational Recommendation Systems with Alternative Relevant Items

  In Conversational Recommendation Systems (CRS), a user provides feedback on recommended items at each turn, leading the CRS towards improved recommendations.

• Fifth Workshop on Recommender Systems for Human Resources (RecSys in HR 2025)
• Fine-tuning for Inference-efficient Calibrated Recommendations

  Calibration is the degree to which a recommender system is able to match the distribution of a certain item attribute among the items consumed by a user with their respective recommendations.

• First International Workshop on Data Quality-Aware Multimodal Recommendation (DaQuaMRec)

  The integration of rich, multimodal signals—spanning visual, textual, and acoustic information—represents a significant evolution for recommender systems, promising more nuanced and personalized user experiences.

• Flights Pricelock Fee Recommendation on Online Travel Agent Platform

  In this study, we present a neural network (NN) based recommender system with novel custom loss function developed to recommend fee for its pricelock product.

• From Previous Plays to Long-Term Tastes: Exploring the Long-term Reliability of Recommender Systems Simulations for Children

  Studying the interplay of children and recommender systems (RS) is ethically and practically challenging, making simulation a promising alternative for exploration.

• Full-Page Recommender: A Modular Framework for Multi-Carousel Recommendations

  Full-page layouts with multiple carousels are widely used in video streaming platforms, yet understudied in recommender systems research.

• Generalized User Representations for Large-Scale Recommendations and Downstream Tasks

  Accurately capturing diverse user preferences at scale is a core challenge for large-scale recommender systems like Spotify’s, given the complexity and variability of user behavior.

• GenSAR: Unifying Balanced Search and Recommendation with Generative Retrieval

  Many commercial platforms provide both search and recommendation (S&R) services to meet different user needs.

• GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization

  Generative models have recently demonstrated strong potential in multi-behavior recommendation systems, leveraging the expressive power of transformers and tokenization to generate personalized item sequences.

• GreenFoodLens: Sustainability Labels for Food Recommendation

  Most food recommender systems aim to boost user engagement by analyzing recipe ingredients and users’ past choices.

• Heterogeneous User Modeling for LLM-based Recommendation

  Leveraging Large Language Models (LLMs) for recommendation has demonstrated notable success in various domains, showcasing their potential for open-domain recommendation.

• HiDePCC: A Novel Dual-Pronged Untargeted Attack on Federated Recommendation via Gradient Perturbation and Cluster Crafting

  Federated recommender systems offer privacy benefits by decentralizing user data and preventing direct data sharing among clients.

• Hierarchical Graph Information Bottleneck for Multi-Behavior Recommendation

  In real-world recommendation scenarios, users typically engage with platforms through multiple types of behavioral interactions.

• How Do Users Perceive Recommender Systems' Objectives?

  Multi-objective recommender systems (MORS) aim to optimize multiple criteria while generating recommendations, such as relevance, novelty, diversity, or exploration.

• How Fair is Your Diffusion Recommender Model?

  Diffusion-based learning has settled as a rising paradigm in generative recommendation, outperforming traditional approaches built upon variational autoencoders and generative adversarial networks.

• How Powerful are LLMs to Support Multimodal Recommendation? A Reproducibility Study of LLMRec

  Large language models (LLMs) have been exploited as standalone recommender systems (RSs) and, more recently, as support tools for already existing RSs.

• Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems

  A critical challenge in recommender systems is to establish reliable relationships between offline and online metrics that predict real-world performance.

• Impacts of Mainstream-Driven Algorithms on Recommendations for Children Across Domains: A Reproducibility Study

  Recommender systems research seldom considers children as a user group, and when it does, it is anchored on datasets where children are underrepresented, risking overlooking their interests, favoring those of the majority, i.e., mainstream users.

• Improving Visual Recommendation on E-commerce Platforms Using Vision-Language Models

  On large-scale e-commerce platforms with tens of millions of active monthly users, recommending visually similar products is essential for enabling users to efficiently discover items that align with their preferences.

• In-context Learning for Addressing User Cold-start in Sequential Movie Recommenders

  The user cold-start problem remains a fundamental challenge for sequential recommender systems, particularly in large-scale video streaming services where a substantial portion of users have limited or no historical interaction data.

• Informfully Recommenders - Reproducibility Framework for Diversity-aware Intra-session Recommendations

  Norm-aware recommender systems have gained increased attention, especially for diversity optimization.

• Integrating Individual and Group Fairness for Recommender Systems through Social Choice

  Fairness in recommender systems is a complex concept, involving multiple definitions, different parties for whom fairness is sought, and various scopes over which fairness might be measured.

• Investigating Carbon Footprint of Recommender Systems Beyond Training Time

  The environmental footprint of recommender systems has received growing attention in the research community.

• IP2: Entity-Guided Interest Probing for Personalized News Recommendation

  News recommender systems aim to provide personalized news reading experiences for users based on their reading history.

• Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation

  Natural language interfaces offer a compelling approach for music recommendation, enabling users to express complex preferences conversationally.

• LANCE: Exploration and Reflection for LLM-based Textual Attacks on News Recommender Systems

  News recommender systems rely on rich textual information from news articles to generate user-specific recommendations.

• Large Language Model-based Recommendation System Agents

  A Large Language Model-based agent is an AI assistant that makes use of advanced Tool Calling (TC) and Retrieval Augmented Generation (RAG) techniques to access external tools (e.g., Python code, databases).

• Lasso: Large Language Model-based User Simulator for Cross-Domain Recommendation

  Cross-Domain Recommendation (CDR) aims to mitigate the cold-start problem in target domains by leveraging user interactions from source domains.

• LEAF: Lightweight, Efficient, Adaptive and Flexible Embedding for Large-Scale Recommendation Models

  Deep Learning Recommendation Models (DLRMs) are central to enhancing user engagement and experience with internet and e-commerce companies.

• Learning geometry-aware recommender systems with manifold regularization

  Recent work shows that hyperbolic geometry may be a better option for recommendation systems in some cases due to the natural hierarchy present in user demands.

• Leave No One Behind: Fairness-Aware Cross-Domain Recommender Systems for Non-Overlapping Users

  Cross-domain recommendation (CDR) methods predominantly leverage overlapping users to transfer knowledge from a source domain to a target domain.

• Let It Go? Not Quite: Addressing Item Cold Start in Sequential Recommendations with Content-Based Initialization

  Many sequential recommender systems suffer from the cold start problem, where items with few or no interactions cannot be effectively used by the model due to the absence of a trained embedding.

• Leveraging Explicit Negative Feedback in Large-Scale Recommendation Systems: A Case Study

  What users dislike can be just as important as what they engage with, yet explicit negative user feedback remains underutilized in most recommendation systems.

• Leveraging Geometric Insights in Hyperbolic Triplet Loss for Improved Recommendations

  Recent studies have demonstrated the potential of hyperbolic geometry for capturing complex patterns from interaction data in recommender systems.

• Lift It Up Right: A Recommender System for Safer Lifting Postures

  Work-related musculoskeletal disorders, often caused by poor lifting posture and unsafe manual handling, continue to pose a significant threat to worker health and safety.

• LLM-Powered Nuanced Video Attribute Annotation for Enhanced Recommendations

  This paper presents a case study of deploying Large Language Models (LLMs) as an advanced "annotation" mechanism to achieve nuanced content understanding (e.g., discerning content "vibe") at scale within an industrial short-form video recommendati...

• LLM-RecG: A Semantic Bias-Aware Framework for Zero-Shot Sequential Recommendation

  Zero-shot cross-domain sequential recommendation (ZCDSR) enables predictions in unseen domains without additional training or fine-tuning, addressing the limitations of traditional models in sparse data environments.

• LONGER: Scaling Up Long Sequence Modeling in Industrial Recommenders

  Modeling ultra-long user behavior sequences is critical for capturing both long- and short-term preferences in industrial recommender systems.

• Mapping Stakeholder Needs to Multi-Sided Fairness in Candidate Recommendation for Algorithmic Hiring

  Already before the enactment of the EU AI Act, candidate or job recommendation for algorithmic hiring—semi-automatically matching CVs to job postings—was used as an example of a high-risk application where unfair treatment could result in serious ...

• MDSBR: Multimodal Denoising for Session-based Recommendation

  Multimodal session-based recommendation (SBR) has emerged as a promising direction for capturing user intent using visual and textual item content.

• Minimize Negative Experiences in Video Recommendation Systems with Multimodal Large Language Models

  Detecting and limiting negative user experiences in recommendation systems with survey feedback modeling is difficult due to ultra-sparse, imbalanced, and noisy data.

• Mitigating Latent User Biases in Pre-trained VAE Recommendation Models via On-demand Input Space Transformation

  Recommender systems can unintentionally encode protected attributes (e.g., gender, country, or age) in their learned latent user representations.

• Model Meets Knowledge: Analyzing Knowledge Types for Conversational Recommender Systems

  Conversational Recommender Systems (CRSs) often integrate external knowledge to enhance user preference modeling and item representation learning, addressing the challenge of sparse conversational contexts.

• MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation

  Large language models (LLMs) have emerged as a cutting-edge approach in sequential recommendation, leveraging historical interactions to model dynamic user preferences.

• Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An Industrial Tutorial

  The goal of this tutorial is to provide our perspective on the most recent advances in LLM-powered agents for recommender systems.

• MuRS: 3rd Music Recommender Systems Workshop

  Music recommendation has been a core area of interest within the recommender systems community since its early days.

• Narrative-Driven Itinerary Recommendation: LLM Integration for Immersive Urban Walking

  Sedentary behavior, dubbed the disease of the 21st century, is a ubiquitous force driving chronic illness.

• NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation

  Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships.

• Non-parametric Graph Convolution for Re-ranking in Recommendation Systems

  Graph knowledge has been proven effective in enhancing item rankings in recommender systems (RecSys), particularly during the retrieval stage.

• NORMalize 2025: The Third Workshop on Normative Design and Evaluation of Recommender Systems

  Recommender systems are one of the most widely used applications of artificial intelligence.

• Normative Alignment of Recommender Systems via Internal Label Shift

  Recommender systems optimized solely for user engagement often fail to meet broader normative objectives such as fairness, diversity, or editorial values.

• Not All Impressions Are Created Equal: Psychology-Informed Retention Optimization for Short-Form Video Recommendation

  Recommender systems that are optimized only for short-term engagement can lead to undesirable outcomes and hurt long-term consumer experience.

• Not Just What, But When: Integrating Irregular Intervals to LLM for Sequential Recommendation

  Time intervals between purchasing items are a crucial factor in sequential recommendation tasks, whereas existing approaches focus on item sequences and often overlook by assuming the intervals between items are static.

• Not One News Recommender To Fit Them All: How Different Recommender Strategies Serve Various User Segments

  Many news recommender systems (NRS) adopt a one-recommender-for-all approach, overlooking that users engage with news in fundamentally different ways.

• Off-Policy Evaluation of Candidate Generators in Two-Stage Recommender Systems

  We study offline evaluation of two-stage recommender systems, focusing on the first stage, candidate generation.

• On Inherited Popularity Bias in Cold-Start Item Recommendation

  Collaborative filtering (CF) recommender systems struggle with making predictions on unseen, or ‘cold’, items.

• On the Reliability of Sampling Strategies in Offline Recommender Evaluation

  Offline evaluation plays a central role in benchmarking recommender systems when online testing is impractical or risky.

• Opening the Black Box: Interpretable Remedies for Popularity Bias in Recommender Systems
• Operational Twin-Driven AI Recommender for Strategic Service Planning

  Traditional service management relies heavily on manual processes due to data complexity and human involvement, limiting the impact of AI in strategic planning.

• Paragon: Parameter Generation for Controllable Multi-Task Recommendation

  Commercial recommender systems face the challenge that task requirements from platforms or users often change dynamically (e.g., varying preferences for accuracy or diversity).

• Parameter-Efficient Single Collaborative Branch for Recommendation

  Recommender Systems (RS) often rely on representations of users and items in a joint embedding space and on a similarity metric to compute relevance scores.

• Personalized Image Generation for Recommendations Beyond Catalogs

  Personalization is central to human-AI interaction, yet current diffusion-based image generation systems remain largely insensitive to user diversity.

• Personalized Persuasion-Aware Explanations in Recommender Systems

  With the increasing accuracy of recommender systems (RSs) in providing recommendations based on user preferences and past behaviors, there is a growing need for generating appropriate explanations to facilitate effective decision-making.

• Popularity‑Bias Vulnerability: Semi‑Supervised Label Inference Attack on Federated Recommender Systems

  Organizations are increasingly applying Vertical Federated Learning (VFL) to enhance recommender systems without sharing raw data among themselves.

• Privacy Risks of LLM-Empowered Recommender Systems: An Inversion Attack Perspective

  The large language model (LLM) powered recommendation paradigm has been proposed to address the limitations of traditional recommender systems (RecSys), which often struggle to handle cold-start users or items with new IDs.

• Privacy-Preserving Social Recommendation: Privacy Leakage and Countermeasure

  Social recommendation systems generally utilize two types of data, user-item interaction matrices (R) from rating platform (P0), and user-user social graphs (S) from social platform (P1).

• R4ec: A Reasoning, Reflection, and Refinement Framework for Recommendation Systems

  Harnessing Large Language Models (LLMs) for recommendation systems has emerged as a prominent avenue, drawing substantial research interest.

• RankGraph: Unified Heterogeneous Graph Learning for Cross-Domain Recommendation

  Cross-domain recommendation systems face the challenge of integrating fine-grained user and item relationships across various product domains.

• Recommendation and Temptation

  Traditional recommender systems based on revealed preferences often fail to capture the fundamental duality in user behavior, where consumption choices are driven by both inherent value (enrichment) and instant appeal (temptation).

• Recommendation Is a Dish Better Served Warm

  In modern recommender systems, experimental settings typically include filtering out cold users and items based on a minimum interaction threshold.

• Recommender Systems for Digital Humanities and Archives: Multistakeholder Evaluation, Scholarly Information Needs, and Multimodal Similarity

  Recommender systems (RecSys) in digital humanities (DH) and archives face unique challenges, including balancing competing stakeholder values, serving complex scholarly information needs, and modeling multimodal historical artifacts.

• RecPS: Privacy Risk Scoring for Recommender Systems

  Recommender systems (RecSys) have become an essential component of many web applications.

• RecSys Challenge 2025: Universal Behavioral Profiles for Recommender Systems

  The RecSys Challenge 2025 promotes a unified approach to behavior modeling by introducing Universal Behavioral Profiles.

• Recurrent Autoregressive Linear Model for Next-Basket Recommendation

  Next-basket recommendation aims to predict the (sets of) items that a user is most likely to purchase during their next visit, capturing both short-term sequential patterns and long-term user preferences.

• RecViz: Intuitive Graph-based Visual Analytics for Dataset Exploration and Recommender System Evaluation

  We present RecViz, a novel web application designed to support qualitative analysis of recommender system performance on large datasets.

• Rethinking Subjective Features in Recommender Systems: Personal Views Over Aggregated Values

  Subjective features of content items, such as emotional resonance and aesthetic quality, have become increasingly important in recommender systems (RecSys), as the field moves beyond objective content and behavioral signals.

• Revisiting Prompt Engineering: A Comprehensive Evaluation for LLM-based Personalized Recommendation

  Large language models (LLMs) can perform recommendation tasks by taking prompts written in natural language as input.

• Revisiting the Performance of Graph Neural Networks for Session-based Recommendation

  Graph Neural Networks (GNNs) have shown impressive performance in various domains.

• RicciFlowRec: A Geometric Root Cause Recommender Using Ricci Curvature on Financial Graphs

  We propose RicciFlowRec, a geometric recommendation framework that performs root cause attribution via Ricci curvature and flow on dynamic financial graphs.

• SAGEA: Sparse Autoencoder-based Group Embeddings Aggregation for Fairness-Preserving Group Recommendations

  Group recommender systems (GRS) deliver suggestions to users who plan to engage in activities together, rather than individually.

• Scalable Data Debugging for Neighborhood-based Recommendation with Data Shapley Values

  Machine learning-powered recommendation systems help users find items they like.

• Scaling Generative Recommendations with Context Parallelism on Hierarchical Sequential Transducers

  Large-scale recommendation systems are pivotal to process an immense volume of daily user interactions, requiring the effective modeling of high cardinality and heterogeneous features to ensure accurate predictions.

• Scaling Retrieval for Web-Scale Recommenders: Lessons from Inverted Indexes to Embedding Search

  Web-scale search and recommendation systems depend on efficient retrieval to manage massive datasets and user traffic.

• Second International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2025)

  In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change.

• Semantic IDs for Joint Generative Search and Recommendation

  Generative models powered by Large Language Models (LLMs) are emerging as a unified solution for powering both recommendation and search tasks.

• Semantic IDs for Music Recommendation

  Training recommender systems for next-item recommendation often requires unique embeddings to be learned for each item, which may take up most of the trainable parameters for a model.

• SEMORec: A Scalarized Efficient Multi-Objective Recommendation Framework

  Recommendation systems in multi-stakeholder environments often require optimizing for multiple objectives simultaneously to meet supplier and consumer demands.

• SGCL: Unifying Self-Supervised and Supervised Learning for Graph Recommendation

  Recommender systems (RecSys) are essential for online platforms, providing personalized suggestions to users within a vast sea of information.

• SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference Overhead

  Session-based slate recommendation systems curate ranked sets of items in real-time, adapting to evolving user interactions.

• SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations

  Most industry-scale recommender systems face critical cold-start challenges—new items lack interaction history, making it difficult to distribute them in a personalized manner.

• Standard Practices for Data Processing and Multimodal Feature Extraction in Recommendation with DataRec and Ducho (D&D4Rec)

  Recommendation pipelines involve several stages that can critically affect performance and reproducibility.

• Suggest, Complement, Inspire: Story of Two-Tower Recommendations at Allegro.com

  Building large-scale e-commerce recommendation systems requires addressing three key technical challenges: (1) designing a universal recommendation architecture across dozens of placements, (2) decreasing excessive maintenance costs, and (3) manag...

• Tag-augmented Dual-target Cross-domain Recommendation

  Cross-domain recommendation (CDR) has been proposed to alleviate the data sparsity issue in recommendation systems and has garnered substantial research interest.

• Test-Time Alignment with State Space Model for Tracking User Interest Shifts in Sequential Recommendation

  Sequential recommendation is essential in modern recommender systems, aiming to predict the next item a user may interact with based on their historical behaviors.

• The 13th International Workshop on News Recommendation and Analytics (INRA 2025)

  News recommender systems are integral to the modern digital information ecosystem, helping users navigate the vast volume of daily content.

• The Future is Sparse: Embedding Compression for Scalable Retrieval in Recommender Systems

  Industry-scale recommender systems face a core challenge: representing entities with high cardinality, such as users or items, using dense embeddings that must be accessible during both training and inference.

• The Hidden Cost of Defaults in Recommender System Evaluation

  Hyperparameter optimization is critical for improving the performance of recommender systems, yet its implementation is often treated as a neutral or secondary concern.

• TIM-Rec: Explicit Sparse Feedback on Multi-Item Upselling Recommendations in an Industrial Dataset of Telco Calls

  Upselling recommendations play a critical role in improving customer engagement and maximizing revenue in the telecommunications industry.

• Time to Split: Exploring Data Splitting Strategies for Offline Evaluation of Sequential Recommenders

  Modern sequential recommender systems, ranging from lightweight transformer-based variants to large language models, have become increasingly prominent in academia and industry due to their strong performance in the next-item prediction task.

• Towards Personality-Aware Explanations for Music Recommendations Using Generative AI

  It is well established that the provision of explanations can positively impact the effectiveness of a recommender system.

• Travel Together, Play Together: Gamifying a Group Recommender System for Tourism

  Gamification is increasingly being used in a variety of domains, such as in education to motivate students learning, in healthcare contexts to help patients follow medical indications or improve healthy habits, or even in tourism to enrich the tou...

• TreatRAG: A Framework for Personalized Treatment Recommendation

  Medication recommendation is a critical function of clinical decision support systems, directly influencing patient safety and treatment efficacy.

• Unified Survey Modeling to Limit Negative User Experiences in Recommendation Systems

  Reducing negative user experiences is crucial for the success of recommendation platforms.

• Unobserved Negative Items in Recommender Systems: Challenges and Solutions for Evaluation and Learning

  Properly conducting offline evaluation is crucial for recommender systems.

• USB-Rec: An Effective Framework for Improving Conversational Recommendation Capability of Large Language Model

  Recently, Large Language Models (LLMs) have been widely employed in Conversational Recommender Systems (CRSs).

• USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations

  Large-scale homepage recommendations face critical challenges from pseudo-negative samples caused by exposure bias, where non-clicks may indicate inattention rather than disinterest.

• User Long-Term Multi-Interest Retrieval Model for Recommendation

  User behavior sequence modeling, which captures user interest from rich historical interactions, is pivotal for industrial recommendation systems.

• VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings

  Multimodal learning plays a critical role in e-commerce recommendation platforms today, enabling accurate recommendations and product understanding.

• Workshop on Context-Aware Recommender Systems

  Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing.

• Workshop on Recommenders in Tourism (RecTour) 2025

  The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year.

• You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control

  Recommenders are significantly shaping online information consumption.

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RecSys 2024

전체 217편 중 180편 (82.9%) — 키워드: recommendation / recommender / recommend

• 11th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'24)

  The primary goal of Recommender Systems is to suggest the most suitable items to a user, aligning them with the user’s interests and needs.

• 12th International Workshop on News Recommendation and Analytics (INRA'24)

  Personalization has changed how we engage with news.

• A Comparative Analysis of Text-Based Explainable Recommender Systems

  One way to increase trust among users towards recommender systems is to provide the recommendation along with a textual explanation.

• A Dataset for Adapting Recommender Systems to the Fashion Rental Economy

  In response to the escalating ecological challenges that threaten global sustainability, there’s a need to investigate alternative methods of commerce, such as rental economies.

• A Hybrid Multi-Agent Conversational Recommender System with LLM and Search Engine in E-commerce

  Multi-agent collaboration is the latest trending method to build conversational recommender systems (CRS), especially with the widespread use of Large Language Models (LLMs) recently.

• A Multi-modal Modeling Framework for Cold-start Short-video Recommendation

  Short video has witnessed rapid growth in the past few years in multimedia platforms.

• A Multimodal Single-Branch Embedding Network for Recommendation in Cold-Start and Missing Modality Scenarios

  Most recommender systems adopt collaborative filtering (CF) and provide recommendations based on past collective interactions.

• A New Perspective in Health Recommendations: Integration of Human Pose Estimation

  In recent years, there has been a growing interest in multimodal and multi-source data due to their ability to introduce heterogeneous information.

• A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph

  Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation.

• A Pre-trained Zero-shot Sequential Recommendation Framework via Popularity Dynamics

  This paper proposes a novel pre-trained framework for zero-shot cross-domain sequential recommendation without auxiliary information.

• A Tool for Explainable Pension Fund Recommendations using Large Language Models

  In this demo, we present a prototype tool designed to help financial advisors recommend private pension funds to investors based on their preferences, offering personalized investment suggestions.

• A Tutorial on Feature Interpretation in Recommender Systems

  Data-driven techniques have greatly empowered recommender systems in different scenarios.

• A Unified Graph Transformer for Overcoming Isolations in Multi-modal Recommendation

  With the rapid development of online multimedia services, especially in e-commerce platforms, there is a pressing need for personalised recommender systems that can effectively encode the diverse multi-modal content associated with each item.

• Accelerating the Surrogate Retraining for Poisoning Attacks against Recommender Systems

  Recent studies have demonstrated the vulnerability of recommender systems to data poisoning attacks, where adversaries inject carefully crafted fake user interactions into the training data of recommenders to promote target items.

• Adaptive Fusion of Multi-View for Graph Contrastive Recommendation

  Recommendation is a key mechanism for modern users to access items of their interests from massive entities and information.

• AI-assisted Coding with Cody: Lessons from Context Retrieval and Evaluation for Code Recommendations

  In this work, we discuss a recently popular type of recommender system: an LLM-based coding assistant.

• AI-based Human-Centered Recommender Systems: Empirical Experiments and Research Infrastructure

  This is a dissertation plan built around human-centered empirical experiments evaluating recommender systems (RecSys).

• AltRecSys: A Workshop on Alternative, Unexpected, and Critical Ideas in Recommendation

  The AltRecsys workshop, held in conjunction with the 18th edition of the ACM Conference on Recommender Systems (RecSys) in Bari, Italy, provides a platform for highlighting “alternative” work in recommender systems.

• AMBAR: A dataset for Assessing Multiple Beyond-Accuracy Recommenders

  Nowadays a recommendation model should exploit additional information from both the user and item perspectives, in addition to utilizing user-item interaction data.

• Analyzing User Preferences and Quality Improvement on Bing's WebPage Recommendation Experience with Large Language Models

  Explore Further @ Bing (Web Recommendations) is a web-scale query independent webpage-to-webpage recommendation system with an index size of over 200 billion webpages.

• Are We Explaining the Same Recommenders? Incorporating Recommender Performance for Evaluating Explainers

  Explainability in recommender systems is both crucial and challenging.

• Balancing Habit Repetition and New Activity Exploration: A Longitudinal Micro-Randomized Trial in Physical Activity Recommendations

  As repetition of activities can establish habits and exploration of new ones can provide a healthy variety, we investigate how a recommender system for physical activities can optimally balance these two approaches.

• beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender Systems

  Recommender systems often use text-side information to improve their predictions, especially in cold-start or zero-shot recommendation scenarios, where traditional collaborative filtering approaches cannot be used.

• Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations

  Randomly-hashed item ids are used ubiquitously in recommendation models.

• Bias in Book Recommendation

  Recent studies have shown that recommendation systems commonly suffer from popularity bias.

• Biased User History Synthesis for Personalized Long-Tail Item Recommendation

  Recommendation systems connect users to items and create value chains in the internet economy.

• Bootstrapping Conditional Retrieval for User-to-Item Recommendations

  User-to-item retrieval has been an active research area in recommendation system, and two tower models are widely adopted due to model simplicity and serving efficiency.

• Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?

  Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches.

• Bridging Viewpoints in News with Recommender Systems

  News Recommender systems (NRSs) aid in decision-making in news media.

• Calibrating the Predictions for Top-N Recommendations

  Well-calibrated predictions of user preferences are essential for many applications.

• CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation

  Traditional recommender systems such as matrix factorization methods have primarily focused on learning a shared dense embedding space to represent both items and user preferences.

• Can Editorial Decisions Impair Journal Recommendations? Analysing the Impact of Journal Characteristics on Recommendation Systems

  Recommendation services for journals help scientists choose appropriate publication venues for their research results.

• CAPRI-FAIR: Integration of Multi-sided Fairness in Contextual POI Recommendation Framework

  Point-of-interest (POI) recommendation considers spatio-temporal factors like distance, peak hours, and user check-ins.

• CEERS: Counterfactual Evaluations of Explanations in Recommender Systems

  The increasing focus on explainability within ethical AI, mandated by frameworks such as GDPR, highlights the critical need for robust explanation mechanisms in Recommender Systems (RS).

• Co-optimize Content Generation and Consumption in a Large Scale Video Recommendation System

  Multi-task prediction models and value models are the de-facto standard ranking components in modern large-scale content recommendation systems.

• Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems

  Over the years, Music Information Retrieval (MIR) has proposed various models pretrained on large amounts of music data.

• Computational Methods for Designing Human-Centered Recommender Systems: A Case Study Approach Intersecting Visual Arts and Healthcare

  Recommender Systems (RecSys) are essential tools in sectors like e-commerce, entertainment, and social media, providing personalized user experiences.

• Conducting Recommender Systems User Studies Using POPROX

  The Platform for OPen Recommendation and Online eXperimentation (POPROX) is a new resource to allow RecSys researchers to conduct online user research without having to develop all of the necessary infrastructure and recruit users.

• Conducting User Experiments in Recommender Systems

  This tutorial provides practical training in designing and conducting online user experiments with recommender systems, and in statistically analyzing the results of such experiments.

• CONSEQUENCES - The 3rd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

  Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them.

• Context-based Entity Recommendation for Knowledge Workers: Establishing a Benchmark on Real-life Data

  In recent decades, Recommender Systems (RS) have undergone significant advancements, particularly in popular domains like movies, music, and product recommendations.

• CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation

  Embedding-based retrieval serves as a dominant approach to candidate item matching for industrial recommender systems.

• Data Augmentation using Reverse Prompt for Cost-Efficient Cold-Start Recommendation

  Recommendation systems that use auxiliary information such as product names and categories have been proposed to address the cold-start problem.

• Deep Recommendation using Graphs

  This tutorial is part of chapters six and eight from my recent book "Deep Recommender Systems with Python", which can be found in https://www.panagiotissymeonidis.com/enbook/index.html.

• Democratizing Urban Mobility Through an Open-Source, Multi-Criteria Route Recommendation System

  Urban navigation systems traditionally optimize for efficiency, thereby overlooking environmental factors and personal preferences.

• Discerning Canonical User Representation for Cross-Domain Recommendation

  Cross-domain recommender systems (CDRs) aim to enhance recommendation outcomes by information transfer across different domains.

• Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models

  Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance.

• DNS-Rec: Data-aware Neural Architecture Search for Recommender Systems

  In the era of data proliferation, efficiently sifting through vast information to extract meaningful insights has become increasingly crucial.

• Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data

  This paper examines the influence of recommender systems on local music representation, discussing prior findings from an empirical study on the LFM-2b public dataset 1.

• Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations

  Sequential recommender systems are an important and demanded area of research.

• Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce

  Coupling latent diffusion based image generation with contextual bandits enables the creation of eye-catching personalized product images at scale that was previously either impossible or too expensive.

• Dynamic Stage-aware User Interest Learning for Heterogeneous Sequential Recommendation

  Sequential recommendation has been widely used to predict users’ potential preferences by learning their dynamic user interests, for which most previous methods focus on capturing item-level dependencies.

• EARL: Workshop on Evaluating and Applying Recommendation Systems with Large Language Models

  This workshop aims to explore the evaluation and application of Large Language Models (LLMs) in recommendation systems (RSs), highlighting innovations, challenges, and future directions, focusing on enhancing RSs through LLM techniques such as pro...

• Economics of Recommender Systems

  This tutorial dives into the economics of recommender systems (RSs), presenting existing and ongoing research on how they influence consumer choices, shape market outcomes, and change the incentives of those who interact with them, whether by desi...

• Efficient Inference of Sub-Item Id-based Sequential Recommendation Models with Millions of Items

  Transformer-based recommender systems, such as BERT4Rec or SASRec, achieve state-of-the-art results in sequential recommendation.

• Embedding Optimization for Training Large-scale Deep Learning Recommendation Systems with EMBark

  Training large-scale deep learning recommendation models (DLRMs) with embedding tables stretching across multiple GPUs in a cluster presents a unique challenge, demanding the efficient scaling of embedding operations that require substantial memor...

• EmbSum: Leveraging the Summarization Capabilities of Large Language Models for Content-Based Recommendations

  Content-based recommendation systems play a crucial role in delivering personalized content to users in the digital world.

• Encouraging Exploration in Spotify Search through Query Recommendations

  At Spotify, search has been traditionally seen as a tool for retrieving content, with the search system optimized for when the user has a specific target in mind.

• End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling

  In modern online platforms, incentives (e.g., discounts, bonus) are essential factors that enhance user engagement and increase platform revenue.

• Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures

  The research domain of recommender systems is rapidly evolving.

• Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention

  The integration of hardware accelerators has significantly advanced the capabilities of modern recommendation systems, enabling the exploration of complex ranking paradigms previously deemed impractical.

• Enhancing Privacy in Recommender Systems through Differential Privacy Techniques

  Recommender systems have become essential tools for addressing information overload in the digital age.

• Enhancing Recommendation Quality of the SASRec Model by Mitigating Popularity Bias

  ZDF is a Public Service Media (PSM) broadcaster in Germany that uses recommender systems on its streaming service platform ZDFmediathek.

• Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning

  Modern music streaming services are heavily based on recommendation engines to serve content to users.

• Enhancing Sequential Music Recommendation with Personalized Popularity Awareness

  In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption.

• Evaluating the Pros and Cons of Recommender Systems Explanations

  Despite the growing interest in explainable AI in the RecSys community, the evaluation of explanations is still an open research topic.

• Evaluation and simplification of text difficulty using LLMs in the context of recommending texts in French to facilitate language learning

  Learning a new language can be challenging.

• Explainability in Music Recommender System

  Recommendation systems play a crucial role in our daily lives, influencing many of our significant and minor decisions.

• Explainable and Faithful Educational Recommendations through Causal Language Modelling via Knowledge Graphs

  The rapid expansion of digital education has significantly increased the need for recommender systems to help learners navigate the extensive variety of available learning resources.

• Explainable Multi-Stakeholder Job Recommender Systems

  Public opinion on recommender systems has become increasingly wary in recent years.

• Exploratory Analysis of Recommending Urban Parks for Health-Promoting Activities

  Parks are essential spaces for promoting urban health, and recommender systems could assist individuals in discovering parks for leisure and health-promoting activities.

• Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender Systems

  Recommender systems have achieved remarkable success in various web applications, such as e-commerce, online advertising, and social media, harnessing the power of big data.

• FAccTRec 2024: The 7th Workshop on Responsible Recommendation

  The 7th Workshop on Responsible Recommendation (FAccTRec 2024) was held in conjunction with the 18th ACM Conference on Recommender Systems on October, 2024 at Bari, Italy, in a hybrid format.

• Fair Reciprocal Recommendation in Matching Markets

  Recommender systems play an increasingly crucial role in shaping people’s opportunities, particularly in online dating platforms.

• FairCRS: Towards User-oriented Fairness in Conversational Recommendation Systems

  Conversational Recommendation Systems (CRSs) enable recommender systems to explicitly acquire user preferences during multi-turn interactions, providing more accurate and personalized recommendations.

• Fairness and Transparency in Music Recommender Systems: Improvements for Artists

  Music streaming services have become one of the main sources of music consumption in the last decade, with recommender systems playing a crucial role.

• Fairness Explanations in Recommender Systems

  Fairness in recommender systems is an emerging area that aims to study and mitigate discriminations against individuals or/and groups of individuals in recommendation engines.

• Fairness Matters: A look at LLM-generated group recommendations

  Recommender systems play a crucial role in how users consume information, with group recommendation receiving considerable attention.

• FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations

  Privacy protection in recommendation systems is gaining increasing attention, for which federated learning has emerged as a promising solution.

• First International Workshop on Recommender Systems for Sustainability and Social Good (RecSoGood 2024)

  In the rapidly evolving landscape of technology and sustainability, leveraging Recommender Systems has emerged as a powerful tool for driving positive change.

• Fourth Workshop on Recommender Systems for Human Resources (RecSys in HR 2024)
• From Clicks to Carbon: The Environmental Toll of Recommender Systems

  As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent.

• GenUI(ne) CRS: UI Elements and Retrieval-Augmented Generation in Conversational Recommender Systems with LLMs

  Previous research has used Large Language Models (LLMs) to develop personalized Conversational Recommender Systems (CRS) with text-based user interfaces (UIs).

• GLAMOR: Graph-based LAnguage MOdel embedding for citation Recommendation

  Digital publishing’s exponential growth has created vast scholarly collections.

• How to Evaluate Serendipity in Recommender Systems: the Need for a Serendiptionnaire

  Recommender systems can assist in various user tasks and serve diverse values, including exploring the item space.

• Improving Adversarial Robustness for Recommendation Model via Cross-Domain Distributional Adversarial Training

  Recommendation models based on deep learning are fragile when facing adversarial examples (AE).

• Improving Data Efficiency for Recommenders and LLMs

  In recent years, massive transformer-based architectures have driven breakthrough performance in practical applications like autoregressive text-generation (LLMs) and click-prediction (recommenders).

• Improving the Shortest Plank: Vulnerability-Aware Adversarial Training for Robust Recommender System

  Recommender systems play a pivotal role in mitigating information overload in various fields.

• Information-Controllable Graph Contrastive Learning for Recommendation

  In the evolving landscape of recommender systems, Graph Contrastive Learning (GCL) has become a prominent method for enhancing recommendation performance by alleviating the issue of data sparsity.

• Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations

  In this paper, we present a strategy to provide users with explainable cross-domain recommendations (CDR) that exploits large language models (LLMs).

• Is It Really Complementary? Revisiting Behavior-based Labels for Complementary Recommendation

  Complementary recommendation is a type of item-to-item recommendation that recommends what should be purchased together for an item.

• It's (not) all about that CTR: A Multi-Stakeholder Perspective on News Recommender Metrics

  Recommender systems are increasingly used by news media organizations.

• It's Not You, It's Me: The Impact of Choice Models and Ranking Strategies on Gender Imbalance in Music Recommendation

  As recommender systems are prone to various biases, mitigation approaches are needed to ensure that recommendations are fair to various stakeholders.

• Joint Modeling of Search and Recommendations Via an Unified Contextual Recommender (UniCoRn)

  Search and recommendation systems are essential in many services, and they are often developed separately, leading to complex maintenance and technical debt.

• KGGLM: A Generative Language Model for Generalizable Knowledge Graph Representation Learning in Recommendation

  Current recommendation methods based on knowledge graphs rely on entity and relation representations for several steps along the pipeline, with knowledge completion and path reasoning being the most influential.

• Knowledge-Enhanced Multi-Behaviour Contrastive Learning for Effective Recommendation

  Real-world recommendation scenarios usually need to handle diverse user-item interaction behaviours, including page views, adding items into carts, and purchasing activities.

• Large Language Models as Evaluators for Recommendation Explanations

  The explainability of recommender systems has attracted significant attention in academia and industry.

• LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding

  Click-Through Rate (CTR) prediction is crucial for Recommendation System(RS), aiming to provide personalized recommendation services for users in many aspects such as food delivery, e-commerce and so on.

• Learning Personalized Health Recommendations via Offline Reinforcement Learning

  The healthcare industry is strained and would benefit from personalized treatment plans for treating various health conditions (e.g., HIV and diabetes).

• Less is More: Towards Sustainability-Aware Persuasive Explanations in Recommender Systems
• Leveraging LLM generated labels to reduce bad matches in job recommendations

  Negative signals are increasingly employed to enhance recommendation quality.

• Leveraging Monte Carlo Tree Search for Group Recommendation

  Group recommenders aim to provide recommendations that satisfy the collective preferences of multiple users, a challenging task due to the diverse individual tastes and conflicting interests to be balanced.

• LLMs for User Interest Exploration in Large-scale Recommendation Systems

  Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests.

• Low Rank Field-Weighted Factorization Machines for Low Latency Item Recommendation

  Factorization machine (FM) variants are widely used in recommendation systems that operate under strict throughput and latency requirements, such as online advertising systems.

• LyricLure: Mining Catchy Hooks in Song Lyrics to Enhance Music Discovery and Recommendation

  Music Search encounters a significant challenge as users increasingly rely on catchy lines from lyrics to search for both new releases and other popular songs.

• MARec: Metadata Alignment for cold-start Recommendation

  For many recommender systems, the primary data source is a historical record of user clicks.

• MAWI Rec: Leveraging Severe Weather Data in Recommendation

  Inferring user intent in recommender systems can help performance but is difficult because intent is personal and not directly observable.

• MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations

  Multi-view Graph Learning is popular in recommendations due to its ability to capture relationships and connections across multiple views.

• MODEM: Decoupling User Behavior for Shared-Account Video Recommendations on Large Screen Devices

  In scenarios involving sequence recommendations on large screen devices, such as tablets or TVs, the equipment is often shared among multiple users.

• Multi-Behavioral Sequential Recommendation

  Multi-behavioral sequential recommendation has recently attracted increasing attention.

• Multi-Objective Recommendation via Multivariate Policy Learning

  Real-world recommender systems often need to balance multiple objectives when deciding which recommendations to present to users.

• Multi-Preview Recommendation via Reinforcement Learning

  Preview recommendations serve as a crucial shortcut for attracting users’ attention on various systems, platforms, and webpages, significantly boosting user engagement.

• Multimodal Representation Learning for High-Quality Recommendations in Cold-Start and Beyond-Accuracy
• MuRS 2024: 2nd Music Recommender Systems Workshop

  Music recommendation has been relevant to the Recommender Systems (RecSys) community since the early days.

• Neighborhood-Based Collaborative Filtering for Conversational Recommendation

  Conversational recommender systems (CRS) should understand users’ expressed interests, which are frequently semantically rich and knowledge-intensive.

• NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender Systems

  Recommender systems are among the most widely used applications of artificial intelligence.

• Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias

  Short-video recommender systems often exhibit a biased preference to recently released videos.

• Oh, Behave! Country Representation Dynamics Created by Feedback Loops in Music Recommender Systems

  Recent work suggests that music recommender systems are prone to disproportionally frequent recommendations of music from countries more prominently represented in the training data, notably the US.

• One-class recommendation systems with the hinge pairwise distance loss and orthogonal representations

  In one-class recommendation systems, the goal is to learn a model from a small set of interacted users and items and then identify the positively-related (i.e., similar) user-item pairs among a large number of pairs with unknown interactions.

• Optimizing for Participation in Recommender System
• Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems

  This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural network.

• Pay Attention to Attention for Sequential Recommendation

  Transformer-based approaches have demonstrated remarkable success in various sequence-based tasks.

• Personal Values and Community-Centric Environmental Recommender Systems: Enhancing Sustainability Through User Engagement

  The concept of sustainability has become a central focus across multiple sectors, driven by the urgent need to address climate change and protect the environment.

• Positive-Sum Impact of Multistakeholder Recommender Systems for Urban Tourism Promotion and User Utility

  When a multistakeholder recommender system (MRS) is designed to produce sustainable urban tourism promotion, two conflicting goals are of practical interest: (i) to cut down the number of visitors at popular sites and (ii) to satisfy tourists’ pre...

• Promoting Two-sided Fairness with Adaptive Weights for Providers and Customers in Recommendation

  At present, most recommender systems involve two stakeholders, providers and customers.

• Prompt Tuning for Item Cold-start Recommendation

  The item cold-start problem is crucial for online recommender systems, as the success of the cold-start phase determines whether items can transition into popular ones.

• Putting Popularity Bias Mitigation to the Test: A User-Centric Evaluation in Music Recommenders

  Popularity bias is a prominent phenomenon in recommender systems (RS), especially in the music domain.

• ReChorus2.0: A Modular and Task-Flexible Recommendation Library

  With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, models, and task settings.

• Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets

  The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored.

• Recommending Healthy and Sustainable Meals exploiting Food Retrieval and Large Language Models

  Given the rising global concerns about healthy nutrition and environmental sustainability, individuals need more and more support in making good choices concerning their daily meals.

• Recommending Personalised Targeted Training Adjustments for Marathon Runners

  Preparing for the marathon involves many weeks of dedicated training.

• RecSys Challenge 2024: Balancing Accuracy and Editorial Values in News Recommendations

  The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing.

• RecTemp: Temporal Reasoning in Recommendation Systems

  This workshop is dedicated to emphasizing the pivotal role of temporal dynamics in advancing recommender systems across various fields.

• Reflections on Recommender Systems: Past, Present, and Future (INTROSPECTIVES)

  With the RecSys conference now turning 18 years old, the recommender systems (RS) discipline ventures into adulthood.

• ReLand: Integrating Large Language Models' Insights into Industrial Recommenders via a Controllable Reasoning Pool

  Recently, Large Language Models (LLMs) have shown significant potential in addressing the isolation issues faced by recommender systems.

• Repeated Padding for Sequential Recommendation

  Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions.

• RePlay: a Recommendation Framework for Experimentation and Production Use

  Using a single tool to build and compare recommender systems significantly reduces the time to market for new models.

• Reproducibility and Analysis of Scientific Dataset Recommendation Methods

  Datasets play a central role in scholarly communications.

• Reproducibility of LLM-based Recommender Systems: the Case Study of P5 Paradigm

  Recommender systems can significantly benefit from the availability of pre-trained large language models (LLMs), which can serve as a basic mechanism for generating recommendations based on detailed user and item data, such as text descriptions, u...

• Revisiting BPR: A Replicability Study of a Common Recommender System Baseline

  Bayesian Personalized Ranking (BPR), a collaborative filtering approach based on matrix factorization, frequently serves as a benchmark for recommender systems research.

• Revisiting LightGCN: Unexpected Inflexibility, Inconsistency, and A Remedy Towards Improved Recommendation

  Graph Neural Networks (GNNs) have emerged as effective tools in recommender systems.

• Right Tool, Right Job: Recommendation for Repeat and Exploration Consumption in Food Delivery

  From e-commerce to music and news, recommender systems are tailored to specific scenarios.

• RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender Systems

  In recent years, recommender systems have become indispensable tools in various domains, aiding users in discovering relevant content amidst the overwhelming amount of available material.

• RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems

  Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational resources.

• Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related Venues

  Rs4rs is a web application designed to perform semantic search on recent papers from top conferences and journals related to Recommender Systems.

• Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs

  Scalability issue plays a crucial role in productionizing modern recommender systems.

• Scaling Law of Large Sequential Recommendation Models

  Scaling of neural networks has recently shown great potential to improve the model capacity in various fields.

• SeCor: Aligning Semantic and Collaborative Representations by Large Language Models for Next-Point-of-Interest Recommendations

  The widespread adoption of location-based applications has created a growing demand for point-of-interest (POI) recommendation, which aims to predict a user’s next POI based on their historical check-in data and current location.

• Self-Attentive Sequential Recommendations with Hyperbolic Representations

  In recent years, self-attentive sequential learning models have surpassed conventional collaborative filtering techniques in next-item recommendation tasks.

• Self-Auxiliary Distillation for Sample Efficient Learning in Google-Scale Recommenders

  Industrial recommendation systems process billions of daily user feedback which are complex and noisy.

• Sixth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)

  Recommender systems, though widely used, often struggle to engage users effectively.

• Sliding Window Training - Utilizing Historical Recommender Systems Data for Foundation Models

  Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years.

• Social Choice for Heterogeneous Fairness in Recommendation

  Algorithmic fairness in recommender systems requires close attention to the needs of a diverse set of stakeholders that may have competing interests.

• Societal Sorting as a Systemic Risk of Recommenders

  Political scientists distinguish between polarization (loosely, people moving further apart along a single dimension) and sorting (an increase in the probabilistic dependence between multiple dimensions of individual difference).

• Supporting Knowledge Workers through Personal Information Assistance with Context-aware Recommender Systems

  Recommender systems are extensively employed across various domains to mitigate information overload by providing personalized content.

• SURE 2024: Workshop on Strategic and Utility-aware REcommendation
• Taming the One-Epoch Phenomenon in Online Recommendation System by Two-stage Contrastive ID Pre-training

  ID-based embeddings are widely used in web-scale online recommendation systems.

• The 1st International Workshop on Risks, Opportunities, and Evaluation of Generative Models in Recommendation (ROEGEN)

  We present an overview of a workshop focused on the exploration of generative models within recommender systems (RS).

• The 6th International Workshop on Health Recommender Systems

  Launched in 2016, the Health Recommender Systems Workshop (HealthRecSys) rapidly became a central forum for discussing the transformative capabilities of personalized recommender systems within the health and care sectors.

• The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation
• The Fault in Our Recommendations: On the Perils of Optimizing the Measurable

  Recommendation systems are widespread, and through customized recommendations, promise to match users with options they will like.

• The MovieLens Beliefs Dataset: Collecting Pre-Choice Data for Online Recommender Systems

  An increasingly important aspect of designing recommender systems involves considering how recommendations will influence consumer choices.

• TLRec: A Transfer Learning Framework to Enhance Large Language Models for Sequential Recommendation Tasks

  Recently, Large Language Models (LLMs) have garnered significant attention in recommendation systems, improving recommendation performance through in-context learning or parameter-efficient fine-tuning.

• Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation

  As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms.

• Toward 100TB Recommendation Models with Embedding Offloading

  Training recommendation models become memory-bound with large embedding tables, and fast GPU memory is scarce.

• Towards Empathetic Conversational Recommender Systems

  Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues.

• Towards Green Recommender Systems: Investigating the Impact of Data Reduction on Carbon Footprint and Algorithm Performances

  This work investigates the path toward green recommender systems by examining the impact of data reduction on both model performance and carbon footprint.

• Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models

  Recommender system plays a vital role in various online services.

• Towards Sustainable Recommendations in Urban Tourism
• Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems

  Traditional recommender systems (RSs) generate suggestions by relying on user preferences and item characteristics.

• Towards Understanding The Gaps of Offline And Online Evaluation Metrics: Impact of Series vs. Movie Recommendations

  In the realm of recommender systems research, offline evaluation metrics like NDCG[4], Recall[1], or Precision[1] are often used to measure the impact.

• Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation

  Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions.

• Understanding Fairness in Recommender Systems: A Healthcare Perspective

  Fairness in AI-driven decision-making systems has become a critical concern, especially when these systems directly affect human lives.

• Unified Denoising Training for Recommendation

  Most existing denoising recommendation methods alleviate noisy implicit feedback (user behaviors) through mainly empirical studies.

• Unleashing the Retrieval Potential of Large Language Models in Conversational Recommender Systems

  Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through interactive natural language interaction.

• Unlocking the Hidden Treasures: Enhancing Recommendations with Unlabeled Data

  Collaborative filtering (CF) stands as a cornerstone in recommender systems, yet effectively leveraging the massive unlabeled data presents a significant challenge.

• User Knowledge Prompt for Sequential Recommendation

  The large language model (LLM) based recommendation system is effective for sequential recommendation, because general knowledge of popular items is included in the LLM.

• Why the Shooting in the Dark Method Dominates Recommender Systems Practice

  The introduction of A/B Testing represented a great leap forward in recommender systems research.

• Workshop on Context-Aware Recommender Systems (CARS) 2024

  Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing.

• Workshop on Recommenders in Tourism (RecTour) 2024

  The Workshop on Recommenders in Tourism (RecTour) has been successfully held in conjunction with the ACM Conference on Recommender Systems (RecSys) since 2016, with the exception of one year.

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RecSys 2023

전체 197편 중 153편 (77.7%) — 키워드: recommendation / recommender / recommend

• 10th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'23)

  Recommender systems (RSs) have undoubtedly played a significant role in addressing the information overload problem by efficiently filtering and suggesting relevant items to users.

• A Lightweight Method for Modeling Confidence in Recommendations with Learned Beta Distributions

  Most recommender systems (RecSys) do not provide an indication of confidence in their decisions.

• A Model-Agnostic Framework for Recommendation via Interest-aware Item Embeddings

  Item representation holds significant importance in recommendation systems, which encompasses domains such as news, retail, and videos.

• A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation

  Sequential recommendation methods play an irreplaceable role in recommender systems which can capture the users’ dynamic preferences from the behavior sequences.

• A Probabilistic Position Bias Model for Short-Video Recommendation Feeds

  Modern web-based platforms often show ranked lists of recommendations to users, in an attempt to maximise user satisfaction or business metrics.

• Acknowledging Dynamic Aspects of Trust in Recommender Systems

  Trust-based recommender systems emerged as a solution to different limitations of traditional recommender systems.

• Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation

  Classical recommender systems often assume that historical data are stationary and fail to account for the dynamic nature of user preferences, limiting their ability to provide reliable recommendations in time-sensitive settings.

• Advancing Automation of Design Decisions in Recommender System Pipelines

  Recommender systems have become essential in domains like streaming services, social media platforms, and e-commerce websites.

• Alleviating the Long-Tail Problem in Conversational Recommender Systems

  Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations.

• An Industrial Framework for Personalized Serendipitous Recommendation in E-commerce

  Classical recommendation methods typically face the filter bubble problem where users likely receive recommendations of their familiar items, making them bored and dissatisfied.

• Analysis Operations for Constraint-based Recommender Systems

  Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams).

• Analyzing Accuracy versus Diversity in a Health Recommender System for Physical Activities: a Longitudinal User Study

  As personalization has great potential to improve mobile health apps, analyzing the effect of different recommender algorithms in the health domain is still in its infancy.

• BehavRec: Workshop on Recommendations for Behavior Change

  The workshop aims to discuss open problems, challenges, and innovative research approaches in the area of persuasive and behavior change recommender systems, that is, recommender systems aimed at modifying people's habits and behavior.

• Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model

  The sequential recommendation task aims to predict the item that user is interested in according to his/her historical action sequence.

• Bootstrapped Personalized Popularity for Cold Start Recommender Systems

  Recommender Systems are severely hampered by the well-known Cold Start problem, identified by the lack of information on new items and users.

• Broadening the Scope: Evaluating the Potential of Recommender Systems beyond prioritizing Accuracy

  Although beyond-accuracy metrics have gained attention in the last decade, the accuracy of recommendations is still considered the gold standard to evaluate Recommender Systems (RSs).

• BVAE: Behavior-aware Variational Autoencoder for Multi-Behavior Multi-Task Recommendation

  A practical recommender system should be able to handle heterogeneous behavioral feedback as inputs and has multi-task outputs ability.

• Challenges for Anonymous Session-Based Recommender Systems in Indoor Environments

  In the last two decades, recommender systems have become more popular since they can provide personalized recommendations in different fields.

• Climbing crags repetitive choices and recommendations

  Outdoor sport climbing in Northern Italy attracts climbers from around the world.

• Co-occurrence Embedding Enhancement for Long-tail Problem in Multi-Interest Recommendation

  Multi-interest recommendation methods extract multiple interest vectors to represent the user comprehensively.

• Complementary Product Recommendation for Long-tail Products

  Identifying complementary relations between products plays a key role in e-commerce Recommender Systems (RS).

• CONSEQUENCES - The 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

  Recommender systems make algorithmic decisions about what will be shown to whom, billions of times every day across the web.

• Contextual Multi-Armed Bandit for Email Layout Recommendation

  We present the use of a contextual multi-armed bandit approach to improve the personalization of marketing emails sent to Wayfair’s customers.

• Contrastive Learning with Frequency-Domain Interest Trends for Sequential Recommendation

  Recently, contrastive learning for sequential recommendation has demonstrated its powerful ability to learn high-quality user representations.

• CR-SoRec: BERT driven Consistency Regularization for Social Recommendation

  In the real world, when we seek our friends’ opinions on various items or events, we request verbal social recommendations.

• Creating the next generation of news experience on ekstrabladet.dk with recommender systems

  With the rise of algorithmic personalization, news organizations are finding it necessary to entrust traditionally held editorial values, such as prioritizing news for readers, to automated systems.

• Customer Lifetime Value Prediction: Towards the Paradigm Shift of Recommender System Objectives

  The ultimate goal of recommender systems is satisfying users’ information needs in the long term.

• Data-free Knowledge Distillation for Reusing Recommendation Models

  A common practice to keep the freshness of an offline Recommender System (RS) is to train models that fit the user’s most recent behaviour while directly replacing the outdated historical model.

• Deep Exploration for Recommendation Systems

  Modern recommendation systems ought to benefit by probing for and learning from delayed feedback.

• Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study

  News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community.

• Delivery Hero Recommendation Dataset: A Novel Dataset for Benchmarking Recommendation Algorithms

  In this paper we propose Delivery Hero Recommendation Dataset (DHRD), a novel real-world dataset for researchers.

• Demystifying Recommender Systems: A Multi-faceted Examination of Explanation Generation, Impact, and Perception

  CCS CONCEPTS

• Denoising Explicit Social Signals for Robust Recommendation

  Social recommender system assumes that user’s preferences can be influenced by their social connections.

• Distribution-based Learnable Filters with Side Information for Sequential Recommendation

  Sequential Recommendation aims to predict the next item by mining out the dynamic preference from user previous interactions.

• Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation

  The conventional single-target Cross-Domain Recommendation (CDR) aims to improve the recommendation performance on a sparser target domain by transferring the knowledge from a source domain that contains relatively richer information.

• DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender

  Cross-Domain Sequential Recommendation(CDSR) aims to generate accurate predictions for future interactions by leveraging users’ cross-domain historical interactions.

• EasyStudy: Framework for Easy Deployment of User Studies on Recommender Systems

  Improvements in the recommender systems (RS) domain are not possible without a thorough way to evaluate and compare newly proposed approaches.

• Enhanced Privacy Preservation for Recommender Systems

  1 RESEARCH CHALLENGES Recommender systems are widely used in different applications to provide personalized suggestions to users.

• Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

  Transformer and its variants are a powerful class of architectures for sequential recommendation, owing to their ability of capturing a user’s dynamic interests from their past interactions.

• Equivariant Contrastive Learning for Sequential Recommendation

  Contrastive learning (CL) benefits the training of sequential recommendation models with informative self-supervision signals.

• Everyone's a Winner! On Hyperparameter Tuning of Recommendation Models

  The performance of a recommender system algorithm in terms of common offline accuracy measures often strongly depends on the chosen hyperparameters.

• Explainable Graph Neural Network Recommenders; Challenges and Opportunities

  Graph Neural Networks (GNNs) have demonstrated significant potential in recommendation tasks by effectively capturing intricate connections among users, items, and their associated features.

• Exploring False Hard Negative Sample in Cross-Domain Recommendation

  Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance.

• Exploring Unlearning Methods to Ensure the Privacy, Security, and Usability of Recommender Systems

  Machine learning algorithms have proven highly effective in analyzing large amounts of data and identifying complex patterns and relationships.

• FAccTRec 2023: The 6th Workshop on Responsible Recommendation

  The 6th Workshop on Responsible Recommendation (FAccTRec 2023) was held in conjunction with the 17th ACM Conference on Recommender Systems on September, 2023 at Singapore, in a hybrid format.

• Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets

  In matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform.

• Fifth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)

  Recommender systems have become ubiquitous in daily life, but their limitations in interacting with human users have become evident.

• Fifth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2023

  Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout.

• From Research to Production: Towards Scalable and Sustainable Neural Recommendation Models on Commodity CPU Hardware

  In the last decade, large-scale deep learning has fundamentally transformed industrial recommendation systems.

• Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item Recommendation

  End-to-end retrieval models, such as Tree-based Models (TDM) and Deep Retrieval (DR), have attracted a lot of attention, but they cannot handle cold-start and long-tail item recommendation scenarios well.

• Generative Learning Plan Recommendation for Employees: A Performance-aware Reinforcement Learning Approach

  With the rapid development of enterprise Learning Management Systems (LMS), more and more companies are trying to build enterprise training and course learning platforms for promoting the career development of employees.

• Generative Next-Basket Recommendation

  Next-basket Recommendation (NBR) refers to the task of predicting a set of items that a user will purchase in the next basket.

• Goal-Oriented Multi-Modal Interactive Recommendation with Verbal and Non-Verbal Relevance Feedback

  Interactive recommendation enables users to provide verbal and non-verbal relevance feedback (such as natural-language critiques and likes/dislikes) when viewing a ranked list of recommendations (such as images of fashion products), in order to gu...

• Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations

  The Creator Economy faces concerning levels of unfairness.

• gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative Sampling

  A large catalogue size is one of the central challenges in training recommendation models: a large number of items makes them memory and computationally inefficient to compute scores for all items during training, forcing these models to deploy ne...

• Hessian-aware Quantized Node Embeddings for Recommendation

  Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems.

• Heterogeneous Knowledge Fusion: A Novel Approach for Personalized Recommendation via LLM

  The analysis and mining of user heterogeneous behavior are of paramount importance in recommendation systems.

• How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective

  Multi-list interfaces are widely used in recommender systems, especially in industry, showing collections of recommendations, one below the other, with items that have certain commonalities.

• HUMMUS: A Linked, Healthiness-Aware, User-centered and Argument-Enabling Recipe Data Set for Recommendation

  The overweight and obesity rate is increasing for decades worldwide.

• Identifying Controversial Pairs in Item-to-Item Recommendations

  Recommendation systems in large-scale online marketplaces are essential to aiding users in discovering new content.

• Improving Group Recommendations using Personality, Dynamic Clustering and Multi-Agent MicroServices

  The complexity associated to group recommendations needs strategies to mitigate several problems, such as the group's heterogeinity and conflicting preferences, the emotional contagion phenomenon, the cold-start problem, and the group members’ nee...

• Improving Recommender Systems Through the Automation of Design Decisions

  Recommender systems developers are constantly faced with difficult design decisions.

• Incorporating Time in Sequential Recommendation Models

  Sequential models are designed to learn sequential patterns in data based on the chronological order of user interactions.

• Initiative transfer in conversational recommender systems

  Conversational recommender systems (CRS) are increasingly designed to offer mixed-initiative dialogs in which the user and the system can take turns in starting a communicative exchange, for example, by asking questions or stating preferences.

• Integrating Item Relevance in Training Loss for Sequential Recommender Systems

  Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest.

• Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation

  We consider the problem of sequential recommendation, where the current recommendation is made based on past interactions.

• Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation

  Music listening sessions often consist of sequences including repeating tracks.

• Interface Design to Mitigate Inflation in Recommender Systems

  Recommendation systems rely on user-provided data to learn about item quality and provide personalized recommendations.

• Interpretable User Retention Modeling in Recommendation

  Recommendation usually focuses on immediate accuracy metrics like CTR as training objectives.

• Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit

  LensKit is one of the first and most popular Recommender System libraries.

• InTune: Reinforcement Learning-based Data Pipeline Optimization for Deep Recommendation Models

  Deep learning-based recommender models (DLRMs) have become an essential component of many modern recommender systems.

• Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation

  The remarkable achievements of Large Language Models (LLMs) have led to the emergence of a novel recommendation paradigm — Recommendation via LLM (RecLLM).

• KGTORe: Tailored Recommendations through Knowledge-aware GNN Models

  Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms.

• Knowledge-Aware Recommender Systems based on Multi-Modal Information Sources

  The last few years showed a growing interest in the design and development of Knowledge-Aware Recommender Systems (KARSs).

• Knowledge-based Multiple Adaptive Spaces Fusion for Recommendation

  Since Knowledge Graphs (KGs) contain rich semantic information, recently there has been an influx of KG-enhanced recommendation methods.

• Large Language Model Augmented Narrative Driven Recommendations

  Narrative-driven recommendation (NDR) presents an information access problem where users solicit recommendations with verbose descriptions of their preferences and context, for example, travelers soliciting recommendations for points of interest w...

• Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences

  Traditional recommender systems leverage users’ item preference history to recommend novel content that users may like.

• Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders

  Sequential recommenders have been widely used in industry due to their strength in modeling user preferences.

• Leveraging Large Language Models for Sequential Recommendation

  Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches.

• LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation

  Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems.

• LLM Based Generation of Item-Description for Recommendation System

  The description of an item plays a pivotal role in providing concise and informative summaries to captivate potential viewers and is essential for recommendation systems.

• Localify.org: Locally-focus Music Artist and Event Recommendation

  Cities with strong local music scenes enjoy many social and economic benefits.

• Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations

  Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals.

• M3REC: A Meta-based Multi-scenario Multi-task Recommendation Framework

  Users in recommender systems exhibit multi-behavior in multiple business scenarios on real-world e-commerce platforms.

• Masked and Swapped Sequence Modeling for Next Novel Basket Recommendation in Grocery Shopping

  Next basket recommendation (NBR) is the task of predicting the next set of items based on a sequence of already purchased baskets.

• Multi-Relational Contrastive Learning for Recommendation

  Personalized recommender systems play a crucial role in capturing users’ evolving preferences over time to provide accurate and effective recommendations on various online platforms.

• Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation

  Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable.

• Multiple Connectivity Views for Session-based Recommendation

  Session-based recommendation (SBR), which makes the next-item recommendation based on previous anonymous actions, has drawn increasing attention.

• MuRS: Music Recommender Systems Workshop

  1 WORKSHOP DESCRIPTION AND RATIONALE Music recommendation has been a prominent use case in the RecSys community since the early days [4, 14].

• Navigating the Feedback Loop in Recommender Systems: Insights and Strategies from Industry Practice

  Understanding and measuring the impact of feedback loops in industrial recommender systems is challenging, leading to the underestimation of their deterioration.

• Nonlinear Bandits Exploration for Recommendations

  The paradigm of framing recommendations as (sequential) decision-making processes has gained significant interest.

• NORMalize: The First Workshop on Normative Design and Evaluation of Recommender Systems

  Recommender systems are among the most widely used applications of artificial intelligence.

• Of Spiky SVDs and Music Recommendation

  The truncated singular value decomposition is a widely used methodology in music recommendation for direct similar-item retrieval and downstream tasks embedding musical items.

• On Challenges of Evaluating Recommender Systems in an Offline Setting

  In the past 20 years, the area of Recommender Systems (RecSys) has gained significant attention from both academia and industry.

• On the Consistency of Average Embeddings for Item Recommendation

  A prevalent practice in recommender systems consists of averaging item embeddings to represent users or higher-level concepts in the same embedding space.

• On the Consistency, Discriminative Power and Robustness of Sampled Metrics in Offline Top-N Recommender System Evaluation

  Negative item sampling in offline top-n recommendation evaluation has become increasingly wide-spread, but remains controversial.

• Online Matching: A Real-time Bandit System for Large-scale Recommendations

  The last decade has witnessed many successes of deep learning-based models for industry-scale recommender systems.

• Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning

  Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on ...

• ORSUM 2023 - 6th Workshop on Online Recommender Systems and User Modeling

  Modern online platforms for user modeling and recommendation require complex data infrastructures to collect and process data.

• Overcoming Recommendation Limitations with Neuro-Symbolic Integration

  Despite being studied for over twenty years, Recommender Systems (RSs) still suffer from important issues that limit their applicability in real-world scenarios.

• Pairwise Intent Graph Embedding Learning for Context-Aware Recommendation

  Although knowledge graph has shown their effectiveness in mitigating data sparsity in many recommendation tasks, they remain underutilized in context-aware recommender systems (CARS) with the specific sparsity challenges associated with the contex...

• Personalised Recommendations for the BBC iPlayer: Initial approach and current challenges

  BBC iPlayer is one of the most important digital products of the BBC, offering live and on-demand television for audiences in the UK with over 10 million weekly active users.

• Personalized Category Frequency prediction for Buy It Again recommendations

  Buy It Again (BIA) recommendations are crucial to retailers to help improve user experience and site engagement by suggesting items that customers are likely to buy again based on their own repeat purchasing patterns.

• Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation

  As E-commerce and subscription services scale, personalized recommender systems are often needed to further drive long term business growth in acquisition, engagement, and retention of customers.

• Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders

  An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user’s gender or age should not influence the model.

• QUARE: 2nd Workshop on Measuring the Quality of Explanations in Recommender Systems

  QUARE1—measuring the QUality of explAnations in REcommender systems—is the second workshop which focuses on evaluation methodologies for explanations in recommender systems.

• RecAD: Towards A Unified Library for Recommender Attack and Defense

  In recent years, recommender systems have become a ubiquitous part of our daily lives, while they suffer from a high risk of being attacked due to the growing commercial and social values.

• Reciprocal Sequential Recommendation

  Reciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment.

• Recommenders In the wild - Practical Evaluation Methods

  The gap between training a recommender model and actually having a recommender system in production is a topic often neglected.

• ReCon: Reducing Congestion in Job Recommendation using Optimal Transport

  Recommender systems may suffer from congestion, meaning that there is an unequal distribution of the items in how often they are recommended.

• RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems

  As spoken dialog systems like Siri, Alexa and Google Assistant become widespread, it becomes apparent that relying solely on global, one-size-fits-all models of Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Entity Re...

• Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasures

  Reproducibility is an important requirement for scientific progress, and the lack of reproducibility for a large amount of published research can hinder the progress over the state-of-the-art.

• Reproducibility of Multi-Objective Reinforcement Learning Recommendation: Interplay between Effectiveness and Beyond-Accuracy Perspectives

  Providing effective suggestions is of predominant importance for successful Recommender Systems (RSs).

• Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

  Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes.

• Retrieval-augmented Recommender System: Enhancing Recommender Systems with Large Language Models

  Recommender Systems (RSs) play a pivotal role in delivering personalized recommendations across various domains, from e-commerce to content streaming platforms.

• Scalable Deep Q-Learning for Session-Based Slate Recommendation

  Reinforcement learning (RL) has demonstrated great potential to improve slate-based recommender systems by optimizing recommendations for long-term user engagement.

• Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions

  This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling.

• Sequential Recommendation Models: A Graph-based Perspective

  Recommender systems (RS) traditionally leverage the users’ rich interaction data with the system, but ignore the sequential dependency of items.

• SPARE: Shortest Path Global Item Relations for Efficient Session-based Recommendation

  Session-based recommendation aims to predict the next item based on a set of anonymous sessions.

• Stability of Explainable Recommendation

  Explainable Recommendation has been gaining attention over the last few years in industry and academia.

• STAN: Stage-Adaptive Network for Multi-Task Recommendation by Learning User Lifecycle-Based Representation

  Recommendation systems play a vital role in many online platforms, with their primary objective being to satisfy and retain users.

• STRec: Sparse Transformer for Sequential Recommendations

  With the rapid evolution of transformer architectures, researchers are exploring their application in sequential recommender systems (SRSs) and presenting promising performance on SRS tasks compared with former SRS models.

• TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation

  Large Language Models (LLMs) have demonstrated remarkable performance across diverse domains, thereby prompting researchers to explore their potential for use in recommendation systems.

• The Eleventh International Workshop on News Recommendation and Analytics (INRA'23)

  Artificial Intelligence is transforming the news eco-system at a rapid pace.

• Third Workshop on Recommender Systems for Human Resources (RecSys in HR 2023)

  1.1 Motivation The field of Human Resources (HR) is at the forefront of adopting AI technologies.

• Third Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2023)

  Evaluation is important when developing and deploying recommender systems.

• Ti-DC-GNN: Incorporating Time-Interval Dual Graphs for Recommender Systems

  Recommender systems are essential for personalized content delivery and have become increasingly popular recently.

• Time-Aware Item Weighting for the Next Basket Recommendations

  In this paper we study the next basket recommendation problem.

• Topic-Level Bayesian Surprise and Serendipity for Recommender Systems

  A recommender system that optimizes its recommendations solely to fit a user’s history of ratings for consumed items can create a filter bubble, wherein the user does not get to experience items from novel, unseen categories.

• Towards Companion Recommenders Assisting Users' Long-Term Journeys

  With the abundance of the internet content and advances of recommendation techniques, the role of recommendation systems has significantly expanded.

• Towards Health-Aware Fairness in Food Recipe Recommendation

  Food recommendation systems play a crucial role in promoting personalized recommendations designed to help users find food and recipes that align with their preferences.

• Towards Robust Fairness-aware Recommendation

  Due to the progressive advancement of trustworthy machine learning algorithms, fairness in recommender systems is attracting increasing attention and is often considered from the perspective of users.

• Towards Self-Explaining Sequence-Aware Recommendation

  Self-explaining models are becoming an important perk of recommender systems, as they help users understand the reason behind certain recommendations, which encourages them to interact more often with the platform.

• Towards Sustainability-aware Recommender Systems: Analyzing the Trade-off Between Algorithms Performance and Carbon Footprint

  In this paper, we present a comparative analysis of the trade-off between the performance of state-of-the-art recommendation algorithms and their environmental impact.

• Trending Now: Modeling Trend Recommendations

  Modern recommender systems usually include separate recommendation carousels such as ‘trending now’ to list trending items and further boost their popularity, thereby attracting active users.

• Trustworthy Recommender Systems: Technical, Ethical, Legal, and Regulatory Perspectives

  This tutorial provides an interdisciplinary overview about the topics of fairness, non-discrimination, transparency, privacy, and security in the context of recommender systems.

• Tutorial on Large Language Models for Recommendation

  Foundation Models such as Large Language Models (LLMs) have significantly advanced many research areas.

• Two-sided Calibration for Quality-aware Responsible Recommendation

  Calibration in recommender systems ensures that the user’s interests distribution over groups of items is reflected with their corresponding proportions in the recommendation, which has gained increasing attention recently.

• Uncovering ChatGPT's Capabilities in Recommender Systems

  The debut of ChatGPT has recently attracted significant attention from the natural language processing (NLP) community and beyond.

• Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

  In the video recommendation, watch time is commonly adopted as an indicator of user interest.

• Understanding and Modeling Passive-Negative Feedback for Short-video Sequential Recommendation

  Sequential recommendation is one of the most important tasks in recommender systems, which aims to recommend the next interacted item with historical behaviors as input.

• Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models

  In this work, we introduce the notion of Context-Based Prediction Models.

• User Behavior Modeling with Deep Learning for Recommendation: Recent Advances

  User Behavior Modeling (UBM) plays a critical role in user interest learning, and has been extensively used in recommender systems.

• User-Centric Conversational Recommendation: Adapting the Need of User with Large Language Models

  Conversational recommender systems (CRS) promise to provide a more natural user experience for exploring and discovering items of interest through ongoing conversation.

• Using Learnable Physics for Real-Time Exercise Form Recommendations

  Good posture and form are essential for safe and productive exercising.

• VideoRecSys 2023: First Workshop on Large-Scale Video Recommender Systems

  The demand for personalized video recommendations has grown exponentially with the widespread use of video content across various domains, including entertainment, e-commerce, education and social media.

• What We Evaluate When We Evaluate Recommender Systems: Understanding Recommender Systems' Performance using Item Response Theory

  Current practices in offline evaluation use rank-based metrics to measure the quality of top-n recommendation lists.

• When Fairness meets Bias: a Debiased Framework for Fairness aware Top-N Recommendation

  Fairness in the recommendation domain has recently attracted increasing attention due to more and more concerns about the algorithm discrimination and ethics.

• Widespread Flaws in Offline Evaluation of Recommender Systems

  Even though offline evaluation is just an imperfect proxy of online performance – due to the interactive nature of recommenders – it will probably remain the primary way of evaluation in recommender systems research for the foreseeable future, sin...

• Workshop on Context-Aware Recommender Systems 2023

  Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing.

• Workshop on Learning and Evaluating Recommendations with Impressions (LERI)

  Recommender systems typically rely on past user interactions as the primary source of information for making predictions.

• Workshop on Recommenders in Tourism (RecTour) 2023

  The Workshop on Recommenders in Tourism (RecTour) 2023, which is held in conjunction with the 17th issue of the ACM Conference on Recommender Systems (RecSys) in Singapore, addresses specific challenges for recommender systems in the tourism domain.

• ✨ Going Beyond Local: Global Graph-Enhanced Personalized News Recommendations

  Precisely recommending candidate news articles to users has always been a core challenge for personalized news recommendation systems.

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RecSys 2022

전체 125편 중 91편 (72.8%) — 키워드: recommendation / recommender / recommend

• A GPU-specialized Inference Parameter Server for Large-Scale Deep Recommendation Models

  Recommendation systems are of crucial importance for a variety of modern apps and web services, such as news feeds, social networks, e-commerce, search, etc.

• A Lightweight Transformer for Next-Item Product Recommendation

  We apply a transformer using sequential browse history to generate next-item product recommendations.

• A Multi-Stakeholder Recommender System for Rewards Recommendations

  Australia’s largest bank, Commonwealth Bank (CBA) has a large data and analytics function that focuses on building a brighter future for all using data and decision science.

• A Systematic Review and Replicability Study of BERT4Rec for Sequential Recommendation

  BERT4Rec is an effective model for sequential recommendation based on the Transformer architecture.

• Adversary or Friend? An adversarial Approach to Improving Recommender Systems

  Typical recommender systems models are trained to have good average performance across all users or items.

• An Interpretable Neural Network Model for Bundle Recommendations: Doctoral Symposium, Extended Abstract

  A users’ preference for a bundle – a set of items that can be purchased together – can be expressed by the utility of this bundle to the user.

• Aspect Re-distribution for Learning Better Item Embeddings in Sequential Recommendation

  Sequential recommendation has attracted a lot of attention from both academia and industry.

• Augmenting Netflix Search with In-Session Adapted Recommendations

  We motivate the need for recommendation systems that can cater to the members’ in-the-moment intent by leveraging their interactions from the current session.

• BRUCE: Bundle Recommendation Using Contextualized item Embeddings

  A bundle is a pre-defined set of items that are collected together.

• Building and Deploying a Multi-Stage Recommender System with Merlin

  Newcomers to recommender systems often face challenges related to their lack of understanding of how these systems operate in real life.

• Bundle MCR: Towards Conversational Bundle Recommendation

  Bundle recommender systems recommend sets of items (e.g., pants, shirt, and shoes) to users, but they often suffer from two issues: significant interaction sparsity and a large output space.

• CAEN: A Hierarchically Attentive Evolution Network for Item-Attribute-Change-Aware Recommendation in the Growing E-commerce Environment

  Traditional recommendation systems mainly focus on modeling user interests.

• CARS: Workshop on Context-Aware Recommender Systems 2022

  Contextual information has been widely recognized as an important modeling dimension in social sciences and in computing.

• Challenges in Translating Research to Practice for Evaluating Fairness and Bias in Recommendation Systems

  Calls to action to implement evaluation of fairness and bias into industry systems are increasing at a rapid rate.

• Client Time Series Model: a Multi-Target Recommender System based on Temporally-Masked Encoders

  Stitch Fix, an online personal shopping and styling service, creates a personalized shopping experience to meet any purchase occasion across multiple platforms.

• CONSEQUENCES - Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

  Recommender systems are more and more often modelled as repeated decision making processes – deciding which (ranking of) items to recommend to a given user.

• Context and Attribute-Aware Sequential Recommendation via Cross-Attention

  In sparse recommender settings, users’ context and item attributes play a crucial role in deciding which items to recommend next.

• Conversational Recommender System Using Deep Reinforcement Learning

  Deep Reinforcement Learning (DRL) uses the best of both Reinforcement Learning and Deep Learning for solving problems which cannot be addressed by them individually.

• Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders

  While sequential recommender systems achieve significant improvements on capturing user dynamics, we argue that sequential recommenders are vulnerable against substitution-based profile pollution attacks.

• Denoising Self-Attentive Sequential Recommendation

  Transformer-based sequential recommenders are very powerful for capturing both short-term and long-term sequential item dependencies.

• Developing a Human-Centered Framework for Transparency in Fairness-Aware Recommender Systems

  Though recommender systems fundamentally rely on human input and feedback, human-centered research in the RecSys discipline is lacking.

• Do Recommender Systems Make Social Media More Susceptible to Misinformation Spreaders?

  Recommender systems are central to online information consumption and user-decision processes, as they help users find relevant information and establish new social relationships.

• Don't recommend the obvious: estimate probability ratios

  Sequential recommender systems are becoming widespread in the online retail and streaming industry.

• Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations

  Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task.

• EANA: Reducing Privacy Risk on Large-scale Recommendation Models

  Embedding-based deep neural networks (DNNs) are widely used in large-scale recommendation systems.

• Effective and Efficient Training for Sequential Recommendation using Recency Sampling

  Many modern sequential recommender systems use deep neural networks, which can effectively estimate the relevance of items but require a lot of time to train.

• Enhancing Counterfactual Evaluation and Learning for Recommendation Systems

  Evaluating recommendation systems is a task of utmost importance and a very active research field.

• Exploiting Negative Preference in Content-based Music Recommendation with Contrastive Learning

  Advanced music recommendation systems are being introduced along with the development of machine learning.

• Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

  Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems.

• FAccTRec 2022: The 5th Workshop on Responsible Recommendation

  The 5th Workshop on Responsible Recommendation (FAccTRec 2022) was held in conjunction with the 16th ACM Conference on Recommender Systems on September, 2022 at Seattle, USA, in a hybrid format.

• FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial Services

  The FinRec workshop series offers a central forum for the study and discussion of the domain-specific aspects, challenges, and opportunities of RecSys and other related technologies in the financial services domain.

• Flow Moods: Recommending Music by Moods on Deezer

  The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content.

• Fourth Knowledge-aware and Conversational Recommender Systems Workshop (KaRS)

  In the last few years, a renewed interest of the research community in conversational recommender systems (CRSs) has been emerging.

• Fourth Workshop on Recommender Systems in Fashion and Retail - fashionXrecsys2022

  Online Fashion retailers have significantly increased in popularity over the last decade, making it possible for customers to explore hundreds of thousands of products without the need to visit multiple stores or stand in long queues for checkout.

• Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions

  Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user’s next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interaction...

• Hands on Explainable Recommender Systems with Knowledge Graphs

  The goal of this tutorial is to present the RecSys community with recent advances on explainable recommender systems with knowledge graphs.

• Hands-on Reinforcement Learning for Recommender Systems - From Bandits to SlateQ to Offline RL with Ray RLlib

  Reinforcement learning (RL) is gaining traction as a complementary approach to supervised learning for RecSys due to its ability to solve sequential decision-making processes for delayed rewards.

• HELPeR: An Interactive Recommender System for Ovarian Cancer Patients and Caregivers

  Recommending online resources to patients with ovarian cancer and their caregivers is a challenging task.

• Heterogeneous Graph Representation Learning for multi-target Cross-Domain Recommendation

  This paper discusses the current challenges in modeling real world recommendation scenarios and proposes the development of a unified Heterogeneous Graph Representation Learning framework for multi-target Cross-Domain recommendation (HGRL4CDR).

• Improving Recommender Systems with Human-in-the-Loop

  Today, most recommender systems employ Machine Learning to recommend posts, products, and other items, usually produced by the users.

• Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS'22)

  The constant increase in the amount of data and information available on the Web has made the development of systems that can support users in making relevant decisions increasingly important.

• KA-Recsys: Knowledge Appropriate Patient Focused Recommendation Technologies

  1 MOTIVATION AND GOAL Diseases such as diabetes, cancer and heart disease demand that patients take an active role in disease management and seek health information for decision-making and self-management [1].

• Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules

  In this paper, we present a knowledge-aware recommendation framework based on neuro-symbolic graph embeddings that encode first-order logical (FOL) rules.

• Learning Recommendations from User Actions in the Item-poor Insurance Domain

  While personalised recommendations are successful in domains like retail, where large volumes of user feedback on items are available, the generation of automatic recommendations in data-sparse domains, like insurance purchasing, is an open problem.

• Learning to Ride a Buy-Cycle: A Hyper-Convolutional Model for Next Basket Repurchase Recommendation

  The problem of Next Basket Recommendation (NBR) addresses the challenge of recommending items for the next basket of a user, based on her sequence of prior baskets.

• Long-term fairness for Group Recommender Systems with Large Groups

  Group recommender systems (GRS) focus on recommending items to groups of users.

• M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations

  Session-based recommender systems (SBRSs) have shown superior performance over conventional methods.

• MARRS: A Framework for multi-objective risk-aware route recommendation using Multitask-Transformer

  One of the most significant map services in navigation applications is route recommendation.

• Matching Theory-based Recommender Systems in Online Dating

  Online dating platforms provide people with the opportunity to find a partner.

• Measuring Commonality in Recommendation of Cultural Content: Recommender Systems to Enhance Cultural Citizenship

  Recommender systems have become the dominant means of curating cultural content, significantly influencing the nature of individual cultural experience.

• Merlin HugeCTR: GPU-accelerated Recommender System Training and Inference

  In this talk, we introduce Merlin HugeCTR.

• Modeling User Repeat Consumption Behavior for Online Novel Recommendation

  Given a user’s historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in.

• MORS 2022: The Second Workshop on Multi-Objective Recommender Systems

  Recommender Systems are becoming an inherent part of today’s Internet.

• Multi-Modal Dialog State Tracking for Interactive Fashion Recommendation

  Multi-modal interactive recommendation is a type of task that allows users to receive visual recommendations and express natural-language feedback about the recommended items across multiple iterations of interactions.

• Multiobjective Evaluation of Reinforcement Learning Based Recommender Systems

  Movielens dataset has become a default choice for recommender systems evaluation.

• Neural Re-ranking for Multi-stage Recommender Systems

  Re-ranking is one of the most critical stages for multi-stage recommender systems (MRS), which re-orders the input ranking lists by modeling the cross-item interaction.

• Off-Policy Actor-critic for Recommender Systems

  Industrial recommendation platforms are increasingly concerned with how to make recommendations that cause users to enjoy their long term experience on the platform.

• Optimizing product recommendations for millions of merchants

  At Shopify, we serve product recommendations to customers across millions of merchants’ online stores.

• ORSUM 2022 - 5th Workshop on Online Recommender Systems and User Modeling

  Modern online systems for user modeling and recommendation need to continuously deal with complex data streams generated by users at very fast rates.

• ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations

  Recent studies show the benefits of reformulating common machine learning models through the concept of prototypes – representatives of the underlying data, used to calculate the prediction score as a linear combination of similarities of a data p...

• Psychology-informed Recommender Systems Tutorial

  Recommender systems are essential tools to support human decision-making in online information spaces.

• Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender Systems

  Traditional research in Recommender Systems (RSs) often solely focuses on accuracy and a limited number of beyond-accuracy dimensions.

• Query Attribute Recommendation at Amazon Search

  Query understanding models extract attributes from search queries, like color, product type, brand, etc.

• RADio - Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations

  In traditional recommender system literature, diversity is often seen as the opposite of similarity, and typically defined as the distance between identified topics, categories or word models.

• Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

  For a long time, different recommendation tasks require designing task-specific architectures and training objectives.

• Recommendation Systems for Ad Creation: A View from the Trenches

  Creative design is one of the key components of generating engaging content on the web.

• Recommendations: They're in fashion

  Farfetch, the leading online platform for luxury fashion, has spent several years developing a recommender system.

• Recommender Systems and Algorithmic Hate

  Despite increasing reliance on personalization in digital platforms, many algorithms that curate content or information for users have been met with resistance.

• Recommending for a multi-sided marketplace with heterogeneous contents

  Many online personalization platforms today are recommending heterogeneous contents in a multi-sided marketplace consisting of consumers, merchants and other partners.

• RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data

  RecPack is an easy-to-use, flexible and extensible toolkit for top-N recommendation with implicit feedback data.

• RecWork: Workshop on Recommender Systems for the Future of Work

  As organizations increasingly digitize their business processes, the role of recommender systems in work environments is expanding.

• Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation

  Content-based news recommenders learn words that correlate with user engagement and recommend articles accordingly.

• RepSys: Framework for Interactive Evaluation of Recommender Systems

  Making recommender systems more transparent and auditable is crucial for the future adoption of these systems.

• Reusable Self-Attention Recommender Systems in Fashion Industry Applications

  A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets.

• REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale

  Recommendation systems are increasingly modelled as a sequential decision making process, where the system decides which items to recommend to a given user.

• Revisiting the Performance of iALS on Item Recommendation Benchmarks

  Matrix factorization learned by implicit alternating least squares (iALS) is a popular baseline in recommender system research publications.

• Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022)

  Citation for published version (APA): Bogers, T., Graus, D., Kaya, M., Gutiérrez, F., Mesbah, S., & Johnson, C.

• Second Workshop: Perspectives on the Evaluation of Recommender Systems (PERSPECTIVES 2022)

  Evaluation of recommender systems is a central activity when developing recommender systems, both in industry and academia.

• Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales

  Conversational recommender systems offer a way for users to engage in multi-turn conversations to find items they enjoy.

• Streaming Session-Based Recommendation: When Graph Neural Networks meet the Neighborhood

  Frequent updates and model retraining are important in various application areas of recommender systems, e.g., news recommendation.

• Taxonomic Recommendations of Real Estate Properties with Textual Attribute Information

  In this extended abstract, we present an end to end approach for building a taxonomy of home attribute terms that enables hierarchical recommendations of real estate properties.

• The Effect of Feedback Granularity on Recommender Systems Performance

  The main source of knowledge utilized in recommender systems (RS) is users’ feedback.

• TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

  There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation.

• TorchRec: a PyTorch Domain Library for Recommendation Systems

  Recommendation Systems (RecSys) comprise a large footprint of production-deployed AI today.

• Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity

  Federated learning (FL) is an effective mechanism for data privacy in recommender systems that runs machine learning model training on-device.

• Towards Recommender Systems with Community Detection and Quantum Computing

  After decades of being mainly confined to theoretical research, Quantum Computing is now becoming a useful tool for solving realistic problems.

• Towards the Evaluation of Recommender Systems with Impressions

  In Recommender Systems, impressions are a relatively new type of information that records all products previously shown to the users.

• Training and Deploying Multi-Stage Recommender Systems

  Industrial recommender systems are made up of complex pipelines requiring multiple steps including feature engineering and preprocessing, a retrieval model for candidate generation, filtering, a feature store query, a ranking model for scoring, an...

• Tutorial on Offline Evaluation for Group Recommender Systems

  Group Recommender Systems (GRSs), unlike recommendations for individuals, provide suggestions for groups of people.

• Two-Layer Bandit Optimization for Recommendations

  Online commercial app marketplaces serve millions of apps to billions of users in an efficient manner.

• Workshop on Recommenders in Tourism (RecTour)

  The Workshop on Recommenders in Tourism (RecTour) 2021, which is held in conjunction with the 15th ACM Conference on Recommender Systems (RecSys), addresses specific challenges for recommender systems in the tourism domain.

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