Statistical methods for recommender systems / Deepak K. Agarwal and Bee Chung-Chen.

Includes bibliographical references and index.

Part I. Introduction:
1. Introduction;
2. Classical methods;
3. Explore/exploit for recommender problems;
4. Evaluation methods;
Part II. Common Problem Settings:
5. Problem settings and system architecture;
6. Most-popular recommendation;
7. Personalization through feature-based regression;
8. Personalization through factor models;
Part III. Advanced Topics:
9. Factorization through latent dirichlet allocation;
10. Context-dependent recommendation;
11. Multi-objective optimization.

This book provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and the state-of-the-art solutions in personalization.