feat: add InterFormer model for heterogeneous interaction learning in CTR prediction#210
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xiaoxiaoxiaotao wants to merge 3 commits intodatawhalechina:mainfrom
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Implement InterFormer model from paper: - InterFormer: Effective Heterogeneous Interaction Learning for CTR Prediction - Paper: https://arxiv.org/abs/2411.09852 Key components: - Gating: self-gating mechanism for selective information aggregation - PFFN: personalized feed-forward network for context-aware sequence modeling - PMA: pooling by multi-head attention for sequence summarization - CrossArch: cross architecture for bidirectional information flow - InteractionArch: behavior-aware non-sequence embedding learning - SequenceArch: context-aware sequence embedding learning with RoPE
- Update run_amazon_electronics.py to support --model_name interformer - Update README.md to document InterFormer usage and test results table
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Description
This PR adds the implementation of the InterFormer model proposed in the paper InterFormer: Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction (available at https://arxiv.org/abs/2411.09852).
What's Changed
New Files
torch_rechub/models/ranking/interformer.py- Core implementation of the InterFormer modelModified Files
torch_rechub/models/ranking/__init__.py- Add InterFormer exportexamples/ranking/run_amazon_electronics.py- Add runtime support for the InterFormer modelexamples/ranking/README.md- Update related documentationTesting Instructions
Run the example with the following commands:
cd examples/ranking python run_amazon_electronics.py --model_name interformerRelated Issues
None
References