10. References
Use this chapter as the source map for the handbook. The list is grouped by how the material is most useful in practice.
10.1 Guides and courses
- Recommender Systems — A Complete Guide to Machine Learning Models
- 21. Recommender Systems
- Google for Developers: Recommendation Systems course
- NVIDIA Glossary: Recommendation System
- Wikipedia: Recommender system
10.2 Core modeling papers
- Mnih and Salakhutdinov (2007): Probabilistic Matrix Factorization
- Hu, Koren, Volinsky (2008): Collaborative Filtering for Implicit Feedback Datasets
- Koren, Bell, Volinsky (2009): Matrix Factorization Techniques for Recommender Systems
- Koren (2008): Factorization Meets the Neighborhood (SVD++)
- Kula (2015): Metadata Embeddings for User and Item Cold-start Recommendations (LightFM)
- Shaped: The Two-Tower Model for Recommendation Systems: A Deep Dive
- Sumit’s Diary: Two Tower Model Architecture: Current State and Promising Extensions
10.3 Tools and implementations
- Surprise Python package
- Surprise documentation
- Simon Funk (2006): Netflix Update - Try This at Home
- ScaNN