Recommendation Systems
YouTube RecSys
Collaborative filtering with Matrix Factorization + BPR. Explains recommendations with 'Because you watched'.
About the Project
A collaborative filtering recommendation engine using Matrix Factorization with Bayesian Personalized Ranking (MF-BPR). The system learns user and item embeddings from interaction data and provides explainable recommendations by showing which previously watched items drove each suggestion. Trained on MovieLens-100K with a Streamlit web interface for exploration.
Key Features
- Matrix Factorization with BPR (Bayesian Personalized Ranking) loss
- 128-dimensional user and item embeddings
- Explainable recommendations with 'Because you watched' feature
- Adjustable popularity blending for discovery vs relevance
- Interactive Streamlit UI with Recall@K evaluation
Impact
Demonstrates production-ready recommendation system with explainability - a key differentiator for user trust.
Tech Stack
PyTorchStreamlitBPR LossMovieLens-100K
Metrics
128-dim embeddings
Popularity blending
Recall@K evaluation
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