[PDF.76nt] Statistical Methods for Recommender Systems

Statistical Methods for Recommender Systems

[PDF.af29] Statistical Methods for Recommender Systems

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 | #224581 in Books |  Bee Chung Chen Deepak K Agarwal |  2016-02-24 | Original language:English | PDF # 1 |  8.98 x.79 x5.98l,.0 | File type: PDF | 298 pages

 | Statistical Methods for Recommender Systems

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||4 of 4 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 techniq|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 cond

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 ...

You easily download any file type for your device.Statistical Methods for Recommender Systems   |  Deepak K. Agarwal, Bee-Chung Chen. A good, fresh read, highly recommended.

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