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lgli/Deepak K. Agarwal & Bee-Chung Chen - Statistical Methods for Recommender Systems (2015, cj5_2471).mobi
Statistical Methods for Recommender Systems 🔍
Deepak K. Agarwal & Bee-Chung Chen cj5_2471, 2015
English [en] · MOBI · 8.6MB · 2015 · 📕 Book (fiction) · 🚀/lgli/zlib · Save
description
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.
Alternative author
Agarwal, Deepak K.
Alternative publisher
Cambridge University Press (Virtual Publishing)
Alternative edition
United Kingdom and Ireland, United Kingdom
Alternative edition
New York, NY, 2016
Alternative edition
Cambridge, 2016
Alternative edition
1, US, 2016
Alternative description
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.
Alternative description
Deepak K. Agarwal, Bee-chung Chen. Includes Bibliographical References And Index.
date open sourced
2022-01-06
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