Free Statistical Methods For Recommender Systems Ebooks Online
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.
Hardcover: 298 pages
Publisher: Cambridge University Press; 1 edition (February 24, 2016)
Language: English
ISBN-10: 1107036070
ISBN-13: 978-1107036079
Product Dimensions: 6 x 0.8 x 9 inches
Shipping Weight: 2.1 pounds (View shipping rates and policies)
Average Customer Review: 5.0 out of 5 stars See all reviews (2 customer reviews)
Best Sellers Rank: #227,217 in Books (See Top 100 in Books) #12 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Expert Systems #244 in Books > Textbooks > Computer Science > Database Storage & Design #622 in Books > Computers & Technology > Databases & Big Data
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.
Excellent book.. A must-read!
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