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Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.

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.

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Section	Section Description	Page Number
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