Faculty Publications
Collaborative Filtering Recommender Systems
Document Type
Conference
Journal/Book/Conference Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
4321 LNCS
First Page
291
Last Page
324
Abstract
One of the potent personalization technologies powering the adaptive web is collaborative filtering. Collaborative filtering (CF) is the process of filtering or evaluating items through the opinions of other people. CF technology brings together the opinions of large interconnected communities on the web, supporting filtering of substantial quantities of data. In this chapter we introduce the core concepts of collaborative filtering, its primary uses for users of the adaptive web, the theory and practice of CF algorithms, and design decisions regarding rating systems and acquisition of ratings. We also discuss how to evaluate CF systems, and the evolution of rich interaction interfaces. We close the chapter with discussions of the challenges of privacy particular to a CF recommendation service and important open research questions in the field. © Springer-Verlag Berlin Heidelberg 2007.
Department
Department of Computer Science
Original Publication Date
1-1-2007
DOI of published version
10.1007/978-3-540-72079-9_9
Recommended Citation
Schafer, J. Ben; Frankowski, Dan; Herlocker, Jon; and Sen, Shilad, "Collaborative Filtering Recommender Systems" (2007). Faculty Publications. 2706.
https://scholarworks.uni.edu/facpub/2706
