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Practical Recommender Systems

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Recommender systems are practically a necessity for keeping a site's content current, useful, and interesting to visitors. Recommender systems are everywhere, helping you find everything from movies to jobs, restaurants to hospitals, even romance. Practical Recommender Systems goes behind the curtain to show readers how recommender systems work and, more importantly, how to create and apply them for their site. This hands-on guide covers scaling problems and other issues they may encounter as their site grows.

Author(s): Kim Falk
Publisher: Manning Publications
Year: 2019

Language: English
Commentary: True PDF
Pages: 401
City: Shelter Island, NY
Tags: Machine Learning; Python; Recommender Systems; Clustering; SQL; Monitoring; Model Evaluation; Testing; Ranking; User Behavior; Collaborative Filtering; Hybrid Recommenders

Part 1. Getting ready for recommender systems
1. What is a recommender
2. User behavior and how to collect it
3. Monitoring the system
4. Ratings and how to calculate them
5. Non-personalized recommendations
6. The user (and content) who came in from the cold

Part 2. Recommender algorithms
7. Finding similarities among users and among content
8. Collaborative filtering in the neighborhood
9. Evaluating and testing your recommender
10. Content-based filtering
11. Finding hidden genres with matrix factorization
12. Taking the best of all algorithms: Implementing hybrid recommenders
13. Ranking and learning to rank
14. Future of recommender systems