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Movie_Book_Recommender

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The purpose of this project is to build a library in C++ which stores list of books and movies a user has watched. The library will then be used to fine-tune an LLM recommender model to recommend customized movies or books to an end user

Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. License
  5. Contact
  6. Acknowledgments

About The Project

The project will utilize C++ to build out an application for handling the creation and addition of user movie and book libraries. The data will then be stored in a SQL database to add to later or pull data from for use in fine-tunining an LLM. The libraries will then be used to fine-tune an LLM to provide movie or book recommendations tailored to users preferences.

Built With

  • Python
  • C++
  • PyTorch
  • PostgreSQL

Getting Started

Prerequisites (In current state)

Required Libraries/Frameworks:
C++ compiler (GNU C++17 was used for this project)
Libpqxx

Required Hardware:
NVidia GPU (once LLM has been implemented)

Installation

  1. Clone the Repo
git clone https://github.com/zjshermanburke/cppTorchNN
  1. Verify functionality by running in IDE (this project was built and run in VS code on Ubuntu)

Usage

TBD still in progress

Features

  • Build Movie Library
  • Build Book Library
  • Build Data Loader and Saver in C++ (Using PostgreSQL)
  • Build Python PostgreSQL Data Loader
  • Determine which LLM recommender model to utilize
  • Build dataloader based on LLM in Python
  • Create Jupyter notebook to handle fine-tuning in Python
  • Implment user feedback functionality for LLM

License

Distributed under the Unlicense license. See LICENSE.txtfor more information.

Contact

Zachary Sherman-Burke - LinkedIn - zjshermanburke@gmail.com

Project Link: https://github.com/zjshermanburke/TransferLearning_VGG19

Acknowledgments

About

Purpose of this project is to build a library in C++ which stores list of books and movies a user has watched. The library will then be used to fine-tune an LLM recommender model to recommend customized movies or books to an end user

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