Collaborative Filtering Recommender Systems
作者： Dan Frankowski , Jon Herlocker , Shilad Sen , J. Ben Schafer
DOI: 10.1007/978-3-540-72079-9_9
关键词: Recommender system 、 Association rule learning 、 Process (engineering) 、 Personalization 、 Computer science 、 World Wide Web 、 Field (computer science) 、 Recommendation service 、 Collaborative filtering 、 Open research
摘要: One of the potent personalization technologies powering adaptive web is collaborative filtering. Collaborative filtering (CF) process or evaluating items through opinions other people. CF technology brings together large interconnected communities on web, supporting substantial quantities data. In this chapter we introduce core concepts filtering, its primary uses for users theory and practice algorithms, design decisions regarding rating systems acquisition ratings. We also discuss how to evaluate systems, evolution rich interaction interfaces. close with discussions challenges privacy particular a recommendation service important open research questions in field.
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acm.org 参考文章(60)
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