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 ...
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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.
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Add this copy of Statistical Methods for Recommender Systems to cart. $24.99, very good condition, Sold by Goodwill of Silicon Valley rated 5.0 out of 5 stars, ships from San Jose, CA, UNITED STATES, published 2016 by Cambridge University Press.
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Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight there are no rips tears or creases on the cover or the pages.
Add this copy of Statistical Methods for Recommender Systems to cart. $24.99, good condition, Sold by Goodwill of Silicon Valley rated 5.0 out of 5 stars, ships from San Jose, CA, UNITED STATES, published 2016 by Cambridge University Press.
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New. Print on demand Contains: Line drawings, Halftones, Tables, black & white. 66 b/w illus. 18 tables. Intended for professional and scholarly audience.
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Fine. Contains: Line drawings, Halftones, Tables, black & white. 66 b/w illus. 18 tables. Intended for professional and scholarly audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
Add this copy of Statistical Methods for Recommender Systems to cart. $66.87, new condition, Sold by GreatBookPrices rated 4.0 out of 5 stars, ships from Columbia, MD, UNITED STATES, published 2016 by Cambridge University Press.
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New. Contains: Line drawings, Halftones, Tables, black & white. 66 b/w illus. 18 tables. Intended for professional and scholarly audience. In Stock. 100% Money Back Guarantee. Brand New, Perfect Condition, allow 4-14 business days for standard shipping. To Alaska, Hawaii, U.S. protectorate, P.O. box, and APO/FPO addresses allow 4-28 business days for Standard shipping. No expedited shipping. All orders placed with expedited shipping will be cancelled. Over 3, 000, 000 happy customers.
