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 past user responses 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 (multiarmed bandit methods), bilinear random-effects models (matrix factorization), and scalable model fitting using modern computing paradigms such as MapReduce. The authors draw on 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 with which they are directly involved.