Deepak K. Agarwal, Bee-Chung ChenCambridge University Press, 2/24/2016EAN 9781107036079, ISBN10: 1107036070Hardcover, 298 pages, 22.8 x 15.2 x 2 cmLanguage: EnglishDesigning 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.Part I. Introduction1. Introduction2. Classical methods3. Explore/exploit for recommender problems4. Evaluation methodsPart II. Common Problem Settings5. Problem settings and system architecture6. Most-popular recommendation7. Personalization through feature-based regression8. Personalization through factor modelsPart III. Advanced Topics9. Factorization through latent dirichlet allocation10. Context-dependent recommendation11. Multi-objective optimization.
