StatisticalMethodsforRecommenderSystems
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2024-02-24 19:30:06
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文档简介:
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 expe......
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