Abstract
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.
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References
Berkovsky, S.: Decentralized Mediation of User Models for a Better Personalization. In: Proc. of the AH Conference (2006)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. In: Proc. of the SIGIR Conference (1999)
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© 2007 Springer-Verlag Berlin Heidelberg
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Berkovsky, S., Kuflik, T., Ricci, F. (2007). Cross-Domain Mediation in Collaborative Filtering. In: Conati, C., McCoy, K., Paliouras, G. (eds) User Modeling 2007. UM 2007. Lecture Notes in Computer Science(), vol 4511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73078-1_44
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DOI: https://doi.org/10.1007/978-3-540-73078-1_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73077-4
Online ISBN: 978-3-540-73078-1
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