https://github.com/yxtay/matrix-factorization-torch

Matrix Factorization Recommender Models in PyTorch with MovieLens
https://github.com/yxtay/matrix-factorization-torch

Last synced: 15 days ago
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Matrix Factorization Recommender Models in PyTorch with MovieLens

• Host: GitHub
• URL: https://github.com/yxtay/matrix-factorization-torch
• Owner: yxtay
• License: mit
• Created: 2024-02-15T13:40:58.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2025-01-06T23:14:14.000Z (19 days ago)
• Last Synced: 2025-01-07T00:27:06.180Z (19 days ago)
• Language: Python
• Size: 1.09 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# matrix-factorization-pytorch

Matrix Factorization Recommender Models in PyTorch with MovieLens

## Entrypoints

## Losses

## Data

## References

- Google Slides: [Collaborative Filtering with Implicit Feedback](https://docs.google.com/presentation/d/15nLFgmkSEJPXkhLiXExXDByV_lot7bdHAhtqX_qLp7w/)

- [[2101.08769] Item Recommendation from Implicit Feedback](https://arxiv.org/abs/2101.08769)

- [TensorFlow Recommenders Retrieval](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/tasks/Retrieval)

- BPR: [[1205.2618] BPR: Bayesian Personalized Ranking from Implicit Feedback](https://arxiv.org/abs/1205.2618)

- CCL: [[2109.12613] SimpleX: A Simple and Strong Baseline for Collaborative Filtering](https://arxiv.org/abs/2109.12613)

- SSM: [[2201.02327] On the Effectiveness of Sampled Softmax Loss for Item Recommendation](https://arxiv.org/abs/2201.02327)

- DirectAU: [[2206.12811] Towards Representation Alignment and Uniformity in Collaborative Filtering](https://arxiv.org/abs/2206.12811)

- MAWU: [[2308.06091] Toward a Better Understanding of Loss Functions for Collaborative Filtering](https://arxiv.org/abs/2308.06091)

- InfoNCE+, MINE+: [[2312.08520] Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)](https://arxiv.org/abs/2312.08520)

- LogQ correction: [Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations](https://research.google/pubs/sampling-bias-corrected-neural-modeling-for-large-corpus-item-recommendations/)

- MNS: [Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations](https://research.google/pubs/mixed-negative-sampling-for-learning-two-tower-neural-networks-in-recommendations/)

- Hashing Trick: [[0902.2206] Feature Hashing for Large Scale Multitask Learning](https://arxiv.org/abs/0902.2206)

- Hash Embeddings: [[1709.03933] Hash Embeddings for Efficient Word Representations](https://arxiv.org/abs/1709.03933)

- Bloom embeddings: [Compact word vectors with Bloom embeddings](https://explosion.ai/blog/bloom-embeddings)