StatisticalMethodsforRecommenderSystems - (EPUB全文下载)

文件大小：7.33 mb。
文件格式：epub 格式。
书籍内容：

Statistical Methods for Recommender Systems
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 past user responses 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 (multiarmed bandit methods), bilinear random-effects models (matrix factorization), and scalable model fitting using modern computing paradigms such as MapReduce. The authors draw on 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 with which they are directly involved.
DR. DEEPAK K. AGARWAL
is a big data analyst with several 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 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 top-tier journals in statistics.
DR. BEE-CHUNG CHEN
is a leading technologist with extensive industrial and research experience in developing state-of-the-art recommender systems. He has been a key designer of the recommendation algorithms that power the LinkedIn home page and mobile feeds, the Yahoo! home page, Yahoo! News, and other sites. His research areas include recommender systems, data mining, machine learning, and big data analytics.
For Bharati Agarwal and Shiao-Ching Chung
Statistical Methods for Recommender Systems
Deepak K. AgarwalLinkedIn Corporation
Bee-Chung ChenLinkedIn Corporation
32 Avenue of the Americas, New York, NY 10013-2473, USA
Cambridge University Press is part of the University of Cambridge.
It furthers the University’s mission by disseminating knowledge in the pursuit of education, learning, and research at the highest international levels of excellence.
www.cambridge.org
Information on this title: www.c ............

书籍插图：

以上为书籍内容预览，如需阅读全文内容请下载EPUB源文件，祝您阅读愉快。