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Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way.

In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases.

You'll learn:

  • The data essential for building a RecSys
  • How to frame your data and business as a RecSys problem
  • Ways to evaluate models appropriate for your system
  • Methods to implement, train, test, and deploy the model you choose
  • Metrics you need to track to ensure your system is working as planned
  • How to improve your system as you learn more about your users, products, and business case

Table of Contents

  1. Preface
  2. I. Warming Up
  3. 1. Introduction
  4. 2. User-Item Ratings and Framing the Problem
  5. 3. Mathematical Considerations
  6. 4. System Design for Recommending
  7. 5. Putting It All Together: Content Based Recommender
  8. II. Retrieval
  9. 6. Data Processing
  10. 7. Serving Models and Architectures
  11. 8. Putting It All Together: Data Processing and Counting Recommender
  12. III. Ranking
  13. 9. Feature based and counting-based recommendations
  14. 10. Low Rank Methods
  15. 11. Personalized Recommendation Metrics
  16. 12. Training for Ranking
  17. 13. Putting It All Together: Experimenting and Ranking
  18. IV. Serving
  19. 14. Business Logic
  20. 15. Bias in Recommendation Systems
  21. 16. Acceleration Structures
  22. V. The Future of Recs
  23. 17. Sequential Recommenders
  24. 18. What’s Next for Recs?
  25. About the Authors
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