Statistical Methods for Recommender Systems

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.

Deepak K. Agarwal (Author), Bee-Chung Chen (Author)

9781107036079, Cambridge University Press

Hardback, published 24 February 2016

298 pages, 66 b/w illus. 18 tables
23.5 x 15.7 x 2 cm, 0.54 kg

'This book provides a comprehensive guide to state-of-the-art statistical techniques that are used to power recommender systems. … The text is authoritative and well written, with the authors drawing on their extensive experience of researching, implementing and evaluating real-world recommender systems. The book considers the underlying mathematics of the techniques it describes and, as such, is aimed at a readership with a strong background in statistics and cognate subjects. However, while readers without such a background are likely to find the mathematics somewhat challenging, the prose descriptions are highly readable and enable readers to understand the key principles and ideas which underpin the various approaches. This book should be of interest to those involved with recommender systems as well as to those with a broader interest in machine learning.' Patrick Hill, BCS: The Chartered Institute for IT (www.bcs.org)

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.

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.

Subject Areas: Machine learning [UYQM], Computer science [UY], Probability & statistics [PBT]