Collaborative Filtering Recommender Systems Export

@incollection{citeulike:1668956, 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.}, author = {Schafer, J. and Frankowski, Dan and Herlocker, Jon and Sen, Shilad}, citeulike-article-id = {1668956}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-72079-9_9}, citeulike-linkout-1 = {http://www.springerlink.com/content/t87386742n752843}, doi = {10.1007/978-3-540-72079-9_9}, journal = {The Adaptive Web}, keywords = {collaborative-filtering, recommender}, pages = {291--324}, posted-at = {2007-10-28 15:46:01}, priority = {2}, title = {Collaborative Filtering Recommender Systems}, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_9}, year = {2007} }

TY - CHAP ID - citeulike:1668956 L3 - citeulike-article-id:1668956 N2 - 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. JF - The Adaptive Web EP - 324 TI - Collaborative Filtering Recommender Systems SP - 291 KW - collaborative-filtering KW - recommender AU - Schafer, J AU - Frankowski, Dan AU - Herlocker, Jon AU - Sen, Shilad PY - 2007/// UR - http://dx.doi.org/10.1007/978-3-540-72079-9_9 ER -