Statistical methods for recommender systems
By: Agarwal, Deepak K
Title By: Chung-Chen, Bee
Material type: BookPublisher: New York, NY : Cambridge University Press, c2016.Description: xii, 284 p. : ill. ; 24 cm.ISBN: 9781107036079Subject(s): Recommender systems (Information filtering) -- Statistical methods | Expert systems (Computer science) -- Statistical methodsDDC classification: 006.3/3 Online resources: Location MapItem type | Home library | Call number | Status | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 006.33 AG ST (Browse shelf) | Available | T0054725 |
, Shelving location: Main Collection Close shelf browser
006.32 PO PR Programming PyTorch for deep learning : | 006.32 WE DE Deep learning from scratch : | 006.33 AG RE Recommender systems : | 006.33 AG ST Statistical methods for recommender systems | 006.33 GI EX Expert systems : | 006.33 GI EX Expert systems : | 006.33 GI EX Expert systems : |
Includes bibliographical references (p. 265-272) 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.
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.