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Inhaltsangabe
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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Über die Autorinnen und Autoren
Dr Deepak Agarwal is a big data analyst with more than fifteen years of experience developing and deploying state-of-the-art machine learning and statistical methods for improving the relevance of web applications. He is also experienced in conducting new scientific research to solve notoriously difficult big data problems, especially in the areas of recommender systems and computational advertising. He is a Fellow of the American Statistical Association and associate editor of two top-tier journals in statistics.
Dr Bee-Chung Chen is a Senior Staff Engineer and Applied Researcher at LinkedIn. He has been a key designer of the recommendation algorithms that power LinkedIn homepage and mobile feeds, Yahoo! homepage, Yahoo! News and other sites. Dr Chen is a leading technologist with extensive industrial and research experience. His research areas include recommender systems, machine learning and big data analytics.
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Zustand: 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. Bestandsnummer des Verkäufers 118076065
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Hardback. Zustand: New. 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. Bestandsnummer des Verkäufers LU-9781107036079
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Statistical Methods for Recommender Systems
Verlag:
Cambridge University Press, GB, 2016
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ISBN 13: 9781107036079
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Hardback. Zustand: New. 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. Bestandsnummer des Verkäufers LU-9781107036079
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Buch. Zustand: 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. Bestandsnummer des Verkäufers 9781107036079
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