Statistical Methods for Recommender Systems,Used

Statistical Methods for Recommender Systems,Used

SKU: SONG1107036070 In Stock
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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, indepth discussions of current stateoftheart methods such as adaptive sequential designs (multiarmed bandit methods), bilinear randomeffects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such largescale 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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Warranty

We provide a 2-year limited warranty, from the date of purchase for all our products.

If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.

This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).

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