Abstract
In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users’ preferences in the target domain are either scarce or unavailable, but the necessary information for the preferences exists in another domain. Training a system to use such information across domains is shown to produce better performance. Specifically, we represent users’ behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users’ behavior is defined as the items they rated and the items’ rating values. In the next step, a correlation is found between behavior patterns in the source domain and target domain. This mapping is considered a bridge between the two. Based on the correlation and content-attributes of the items, a machine learning model is trained to predict users’ ratings in the target domain. When our approach is compared to the popularity approach and KNN-cross-domain on a real world dataset, the results show that our approach outperforms both methods on an average of 83%.
Chapter PDF
Similar content being viewed by others
References
Berkovsky, S., Kuflik, T., Ricci, F.: Cross-technique mediation of user models. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 21–30. Springer, Heidelberg (2006)
Berkovsky, S., Kuflik, T., Ricci, F.: Entertainment personalization mechanism through cross-domain user modeling. In: Maybury, M., Stock, O., Wahlster, W. (eds.) INTETAIN 2005. LNCS (LNAI), vol. 3814, pp. 215–219. Springer, Heidelberg (2005)
Berkovsky, S., Kuflik, T., Ricci, F.: Cross-domain mediation in collaborative filtering. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 355–359. Springer, Heidelberg (2007)
Berkovsky, S., Kuflik, T., Ricci, F.: Distributed collaborative filtering with domain specialization. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 33–40. ACM (2007)
Emmert-Streib, F., Dehmer, M.: Topological mappings between graphs, trees and generalized trees. Applied Mathematics and Computation 186(2), 1326–1333 (2007)
González, G., López, B., de la Rosa, J.L.: A multi-agent smart user model for cross-domain recommender systems. In: Proceedings of Beyond Personalization (2005)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 5–53 (2004)
Jiang, T., Wang, L., Zhang, K.: Alignment of trees - an alternative to tree edit. In: Crochemore, M., Gusfield, D. (eds.) CPM 1994. LNCS, vol. 807, pp. 75–86. Springer, Heidelberg (1994)
Li, B.: Cross-domain collaborative filtering: A brief survey. In: 2011 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 1085–1086. IEEE (2011)
Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? cross-domain collaborative filtering for sparsity reduction. In: Proceedings of the 21st International Joint Conference on Artifical Intelligence, pp. 2052–2057. Morgan Kaufmann Publishers Inc. (2009)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: CROC: A New Evaluation Criterion for Recommender Systems
Winoto, P., Tang, T.: If you like the devil wears prada the book, will you also enjoy the devil wears prada the movie? a study of cross-domain recommendations. New Generation Computing 26(3), 209–225 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 IFIP International Federation for Information Processing
About this paper
Cite this paper
Biadsy, N., Rokach, L., Shmilovici, A. (2013). Transfer Learning for Content-Based Recommender Systems Using Tree Matching. In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds) Availability, Reliability, and Security in Information Systems and HCI. CD-ARES 2013. Lecture Notes in Computer Science, vol 8127. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40511-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-40511-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40510-5
Online ISBN: 978-3-642-40511-2
eBook Packages: Computer ScienceComputer Science (R0)