Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
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Condition: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Item may be missing bundled media.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
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Condition: New.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: California Books, Miami, FL, U.S.A.
Condition: New.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: As New. Unread book in perfect condition.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
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Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
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Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
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Condition: New. pp. 288.
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Add to basketHardcover. Condition: Brand New. 1st edition. 298 pages. 9.00x6.00x0.50 inches. In Stock.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: AHA-BUCH GmbH, Einbeck, Germany
Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketHardcover. Condition: Brand New. 1st edition. 298 pages. 9.00x6.00x0.50 inches. In Stock. This item is printed on demand.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: Majestic Books, Hounslow, United Kingdom
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Add to basketCondition: New. Print on Demand pp. 288.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: Biblios, Frankfurt am main, HESSE, Germany
Condition: New. PRINT ON DEMAND pp. 288.
Language: English
Published by Cambridge University Press, 2016
ISBN 10: 1107036070 ISBN 13: 9781107036079
Seller: moluna, Greven, Germany
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is for researchers and students in statistics, data mining, computer science, machine learning, marketing and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life.