Items related to Statistical Methods for Recommender Systems
Synopsis
This book provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and the state-of-the-art solutions in personalization.
"synopsis" may belong to another edition of this title.
About the Authors
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.
"About this title" may belong to another edition of this title.
Search results for Statistical Methods for Recommender Systems
Seller Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 24905841-n
Quantity: Over 20 available
Stock Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In. Seller Inventory # ria9781107036079_new
Quantity: Over 20 available
Stock Image
Statistical Methods for Recommender Systems (Hardcover)
Published by
Cambridge University Press, Cambridge, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Hardcover. Condition: new. Hardcover. 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. 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 large-scale systems and state-of-the-art solutions in personalization, explore/exploit, dimension reduction and multi-objective optimization. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781107036079
Quantity: 1 available
Stock Image
Statistical Methods for Recommender Systems
Print on DemandSeller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Hardcover. Condition: Brand New. 1st edition. 298 pages. 9.00x6.00x0.50 inches. In Stock. This item is printed on demand. Seller Inventory # __1107036070
Quantity: 1 available
Seller Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, GB, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: Rarewaves.com UK, London, United Kingdom
Seller rating 4 out of 5 stars
Hardback. Condition: 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. Seller Inventory # LU-9781107036079
Quantity: Over 20 available
Stock Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # I-9781107036079
Quantity: Over 20 available
Seller Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 24905841-n
Quantity: Over 20 available
Stock Image
Statistical Methods for Recommender Systems
Seller: Revaluation Books, Exeter, United Kingdom
Seller rating 5 out of 5 stars
Hardcover. Condition: Brand New. 1st edition. 298 pages. 9.00x6.00x0.50 inches. In Stock. Seller Inventory # x-1107036070
Quantity: 2 available
Stock Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: Toscana Books, AUSTIN, TX, U.S.A.
Seller rating 5 out of 5 stars
Hardcover. Condition: new. Excellent Condition.Excels in customer satisfaction, prompt replies, and quality checks. Seller Inventory # Scanned1107036070
Quantity: 1 available
Stock Image
Statistical Methods for Recommender Systems
Published by
Cambridge University Press, 2016
ISBN 10: 1107036070
ISBN 13: 9781107036079
New
Hardcover
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Print on Demand pp. 288. Seller Inventory # 373864833
Quantity: 4 available