Building Recommendation Systems in Python and Jax 1st Edition by Bryan Bischof, Hector Yee ISBN 1492097993 978-1492097990
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Product details:
ISBN 10: 1492097993
ISBN 13: 978-1492097990
Author: Bryan Bischof, Hector Yee
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, Weights & Biases, and Kafka.
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:
I. Warming Up
1. Introduction
2. User-Item Ratings and Framing the Problem
3. Mathematical Considerations
4. System Design for Recommending
5. Putting It All Together: Content-Based Recommender
II. Retrieval
6. Data Processing
7. Serving Models and Architectures
8. Putting It All Together: Data Processing and Counting Recommender
III. Ranking
9. Feature-Based and Counting-Based Recommendations
10. Low-Rank Methods
11. Personalized Recommendation Metrics
12. Training for Ranking
13. Putting It All Together: Experimenting and Ranking
IV. Serving
14. Business Logic
15. Bias in Recommendation Systems
16. Acceleration Structures
V. The Future of Recs
17. Sequential Recommenders
18. What’s Next for Recs?
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Tags: Bryan Bischof, Hector Yee, Building Recommendation, Python and Jax