Trust-Aware Collaborative Filtering for Recommender Systems Export

@book{citeulike:224397, author = {Massa, Paolo and Avesani, Paolo}, citeulike-article-id = {224397}, citeulike-linkout-0 = {http://www.metapress.com/content/8BAJ2BP1HATVFGKC}, comment = {Abstract Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed ldquoweb of trustrdquo, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings.}, journal = {Lecture Notes in Computer Science}, keywords = {collaborative-filtering, epinions, recommender, recommender-systems, recommender\_systems, reputation, social-networks, systems, trust, trust\_metrics}, month = {October}, pages = {492--508}, posted-at = {2005-06-09 17:27:52}, priority = {0}, title = {Trust-Aware Collaborative Filtering for Recommender Systems}, url = {http://www.metapress.com/content/8BAJ2BP1HATVFGKC}, volume = {3290}, year = {2004} }

TY - BOOK ID - citeulike:224397 L3 - citeulike-article-id:224397 JF - Lecture Notes in Computer Science EP - 508 TI - Trust-Aware Collaborative Filtering for Recommender Systems VL - 3290 SP - 492 KW - collaborative-filtering KW - epinions KW - recommender KW - recommender-systems KW - recommender_systems KW - reputation KW - social-networks KW - systems KW - trust KW - trust_metrics N1 - Abstract Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replaced by a distributed process where the users take the initiative. While the collaborative approach enables the collection of a vast amount of data, a new issue arises: the quality assessment. The elicitation of trust values among users, termed ldquoweb of trustrdquo, allows a twofold enhancement of Recommender Systems. Firstly, the filtering process can be informed by the reputation of users which can be computed by propagating trust. Secondly, the trust metrics can help to solve a problem associated with the usual method of similarity assessment, its reduced computability. An empirical evaluation on Epinions.com dataset shows that trust propagation can increase the coverage of Recommender Systems while preserving the quality of predictions. The greatest improvements are achieved for users who provided few ratings. AU - Massa, Paolo AU - Avesani, Paolo PY - 2004/10// UR - http://www.metapress.com/content/8BAJ2BP1HATVFGKC ER -