References

1. Yehuda Koren, Robert Bell, Chris Volinsky. Matrix Factorization Techniques for Recommender Systems. IEEE Computer, 2009. — The Netflix-Prize-era reference for MF (our SGD version).

2. Yifan Hu, Yehuda Koren, Chris Volinsky. Collaborative Filtering for Implicit Feedback Datasets. ICDM 2008. — Implicit ALS: preference + confidence (our ImplicitALS).

3. Steffen Rendle et al. BPR: Bayesian Personalized Ranking from Implicit Feedback. UAI 2009. — Pairwise learning-to-rank with negative sampling (our BPR).

4. Greg Linden, Brent Smith, Jeremy York. Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 2003. — The item-item neighborhood method at scale.

5. Paul Covington, Jay Adams, Emre Sargin. Deep Neural Networks for YouTube Recommendations. RecSys 2016. — The canonical two-stage (candidate generation + ranking) deep architecture.

6. Xiangnan He et al. Neural Collaborative Filtering. WWW 2017. — Neural generalization of matrix factorization.

7. Maurizio Ferrari Dacrema, Paolo Cremonesi, Dietmar Jannach. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. RecSys 2019. — Why strong, well-tuned baselines matter (and often win).

Tools & libraries

• implicit — fast ALS / BPR (Cython).
• LightFM — hybrid content + collaborative (WARP/BPR).
• FAISS / HNSW / ScaNN — ANN serving for candidate generation.
• OpenSearch / Milvus / Qdrant / Pinecone — vector databases with kNN search.
• TensorFlow Recommenders / TorchRec — two-tower and deep ranking models.

Companion books here

• HNSW from Scratch — the graph-based ANN that serves candidate generation.
• IVF & Product Quantization — compressed ANN for billion-scale catalogs.
• KTS from Scratch — kernel temporal segmentation.

This book's code

• code/recsys.py — all algorithms + metrics.
• code/demo.py — the leaderboard.
• code/recommend_cli.py — the article-recommender CLI.

All depend only on NumPy and the standard library.