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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, 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.
- Sales Rank: #536029 in eBooks
- Published on: 2015-12-31
- Released on: 2016-01-26
- Format: Kindle eBook
About the Author
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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
A great balance between introductory material and specific algorithms for an engineer
By Ryan Tecco
This a great introduction to some of the more cutting edge techniques in recommender systems. It starts with basic structure of various types of recommender systems and then layers in more sophistication. Bayesian methods get a extensive treatment here and explore/exploit techniques are front and center (versus an afterthought in some books and research papers). The treatment of Multi-objective Optimization in recommender systems was unique for a book and very welcome since most real world problems have multiple tradeoffs. If you are an engineer with some statistics knowledge and some patience, you'll find this rewarding.
0 of 6 people found the following review helpful.
Five Stars
By Amazon Customer
Excellent book.. A must-read!
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