Matrix Factorization Recommender Models in PyTorch with MovieLens
- Google Slides: Collaborative Filtering with Implicit Feedback
- [2101.08769] Item Recommendation from Implicit Feedback
- TensorFlow Recommenders Retrieval
- BPR: [1205.2618] BPR: Bayesian Personalized Ranking from Implicit Feedback
- CCL: [2109.12613] SimpleX: A Simple and Strong Baseline for Collaborative Filtering
- SSM: [2201.02327] On the Effectiveness of Sampled Softmax Loss for Item Recommendation
- DirectAU: [2206.12811] Towards Representation Alignment and Uniformity in Collaborative Filtering
- MAWU: [2308.06091] Toward a Better Understanding of Loss Functions for Collaborative Filtering
- InfoNCE+, MINE+: [2312.08520] Revisiting Recommendation Loss Functions through Contrastive Learning (Technical Report)
- LogQ correction: Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
- MNS: Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations
- Hashing Trick: [0902.2206] Feature Hashing for Large Scale Multitask Learning
- Hash Embeddings: [1709.03933] Hash Embeddings for Efficient Word Representations
- Bloom embeddings: Compact word vectors with Bloom embeddings