Explaining collaborative filtering recommendations Export

@inproceedings{Herlocker2000recom, abstract = {Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users.}, address = {New York, NY, USA}, author = {Herlocker, Jonathan L. and Konstan, Joseph A. and Riedl, John}, booktitle = {CSCW '00: Proceedings of the 2000 ACM conference on Computer supported cooperative work}, citeulike-article-id = {975389}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=358995}, citeulike-linkout-1 = {http://dblp.uni-trier.de/rec/bibtex/conf/cscw/HerlockerKR00}, citeulike-linkout-2 = {http://dx.doi.org/10.1145/358916.358995}, doi = {10.1145/358916.358995}, isbn = {1-58113-222-0}, keywords = {collaborative, filtering, recommendation}, location = {Philadelphia, Pennsylvania, United States}, pages = {241--250}, posted-at = {2009-11-09 14:27:02}, priority = {2}, publisher = {ACM}, title = {Explaining collaborative filtering recommendations}, url = {http://dx.doi.org/10.1145/358916.358995}, year = {2000} }

TY - CONF ID - Herlocker2000recom L3 - citeulike-article-id:975389 N2 - Automated collaborative filtering (ACF) systems predict a person's affinity for items or information by connecting that person's recorded interests with the recorded interests of a community of people and sharing ratings between like-minded persons. However, current recommender systems are black boxes, providing no transparency into the working of the recommendation. Explanations provide that transparency, exposing the reasoning and data behind a recommendation. In this paper, we address explanation interfaces for ACF systems - how they should be implemented and why they should be implemented. To explore how, we present a model for explanations based on the user's conceptual model of the recommendation process. We then present experimental results demonstrating what components of an explanation are the most compelling. To address why, we present experimental evidence that shows that providing explanations can improve the acceptance of ACF systems. We also describe some initial explorations into measuring how explanations can improve the filtering performance of users. CY - Philadelphia, Pennsylvania, United States SN - 1-58113-222-0 AD - New York, NY, USA EP - 250 TI - Explaining collaborative filtering recommendations PB - ACM T2 - CSCW '00: Proceedings of the 2000 ACM conference on Computer supported cooperative work SP - 241 KW - collaborative KW - filtering KW - recommendation AU - Herlocker, Jonathan AU - Konstan, Joseph AU - Riedl, John PY - 2000/// UR - http://dx.doi.org/10.1145/358916.358995 ER -