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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Berkovsky, S., Kuflik, T., & Ricci, F. (2007). Cross-domain mediation in collaborative filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4511 LNCS, pp. 355–359). Springer Verlag. https://doi.org/10.1007/978-3-540-73078-1_44
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