TY - GEN
T1 - Cross-domain mediation in collaborative filtering
AU - Berkovsky, Shlomo
AU - Kuflik, Tsvi
AU - Ricci, Francesco
PY - 2007
Y1 - 2007
N2 - One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and aggregating vectors of users' ratings stored by collaborative systems operating in different application domains. The paper presents several mediation approaches and initial experimental evaluation demonstrating that the mediation can improve the accuracy of the generated predictions.
AB - One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and aggregating vectors of users' ratings stored by collaborative systems operating in different application domains. The paper presents several mediation approaches and initial experimental evaluation demonstrating that the mediation can improve the accuracy of the generated predictions.
UR - http://www.scopus.com/inward/record.url?scp=37249090723&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-73078-1_44
DO - 10.1007/978-3-540-73078-1_44
M3 - Conference contribution
AN - SCOPUS:37249090723
SN - 9783540730774
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 355
EP - 359
BT - User Modeling 2007 - 11th International Conference, UM 2007, Proceedings
PB - Springer Verlag
T2 - 11th International on User Modeling Conference, UM 2007
Y2 - 25 June 2007 through 29 June 2007
ER -