Understanding Recommender Systems
A chapter-based guide to recommender systems for data scientists, spanning problem framing, modeling, evaluation, and production design.
Formats
- Handbook version for chapter-by-chapter reading and left-sidebar navigation
- Single-page version for printing or saving as PDF
Chapter Guide
- Why Recommender Systems Matter
- Explicit vs. Implicit Feedback
- Model Families
- Matrix Factorization
- Feature-Rich Recommendation
- Deep Models
- Production Concerns
- Build Sequence
- Summary
- References
- Survey Papers and Further Reading
What This Covers
- problem framing and recommendation surfaces
- explicit, implicit, and sequence-aware recommendation tasks
- content-based, collaborative, contextual, and hybrid approaches
- factorization, ranking objectives, and deep retrieval/ranking models
- evaluation strategy, negative design, and production architecture
Acknowledgments
This handbook draws on a mix of open educational material, public documentation, blog-style explainers, and research papers that make recommender systems easier to learn and easier to operationalize.
In particular, several chapters and figures benefited from:
- Dive into Deep Learning, Chapter 21, especially for task framing, evaluation, and a number of CC BY-SA figures
- Google for Developers: Recommendation Systems course, especially for retrieval, ranking, negative sampling, and production-system tradeoffs
- NVIDIA Glossary: Recommendation System, especially for concise taxonomy and deep-recommender framing
- public technical write-ups and papers that bridge classical collaborative filtering with modern retrieval and ranking systems