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 di...

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Bibliographische Detailangaben

1. Verfasser:	Agarwal, Deepak K. 1973- (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	English
Veröffentlicht:	Cambridge Cambridge University Press 2016
Schlagworte:	Recommender systems (Information filtering) / Statistical methods Expert systems (Computer science) / Statistical methods
Online-Zugang:	BSB01 FHN01 Volltext
Zusammenfassung:	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
Beschreibung:	Title from publisher's bibliographic system (viewed on 05 Feb 2016)
Beschreibung:	1 online resource (xii, 284 pages)
ISBN:	9781139565868
DOI:	10.1017/CBO9781139565868