Abstract
Recommender systems always aim to provide recommendations for a user based on historical ratings collected from a single domain (e.g., movies or books) only, which may suffer from the data sparsity problem. Recently, several recommendation models have been proposed to transfer knowledge by pooling together the rating data from multiple domains to alleviate the sparsity problem, which typically assume that multiple domains share a latent common rating pattern based on the user-item co-clustering. In practice, however, the related domains do not necessarily share such a common rating pattern, and diversity among the related domains might outweigh the advantages of such common pattern, which may result in performance degradations. In this paper, we propose a novel cluster-level based latent factor model to enhance the cross-domain recommendation, which can not only learn the common rating pattern shared across domains with the flexibility in controlling the optimal level of sharing, but also learn the domain-specific rating patterns of users in each domain that involve the discriminative information propitious to performance improvement. To this end, the proposed model is formulated as an optimization problem based on joint nonnegative matrix tri-factorization and an efficient alternating minimization algorithm is developed with convergence guarantee. Extensive experiments on several real world datasets suggest that our proposed model outperforms the state-of-the-art methods for the cross-domain recommendation task.
Chapter PDF
Similar content being viewed by others
References
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 426–434 (2008)
Li, B., Yang, Q., Xue, X.: Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 617–624 (2009)
Li, B.: Cross-domain collaborative filtering: a brief survey. In: 23rd IEEE International Conference on Tools with Artificial Intelligence, pp. 1085–1086 (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, IJCAI 2009, pp. 2052–2057 (2009)
Moreno, O., Shapira, B., Rokach, L., Shani, G.: Talmud: transfer learning for multiple domains. In: 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 425–434 (2012)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: SIGKDD, pp. 126–135 (2006)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2000)
Gao, S., Denoyer, L., Gallinari, P.: Temporal link prediction by integrating content and structure information. In: CIKM 2011, pp. 1169–1174 (2011)
Ding, C., Li, T., Jordan, M.I.: Convex and semi-nonnegative matrix factorizations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2052–2067 (2008)
Si, L., Jin, R.: Flexible mixture model for collaborative filtering. In: ICML 2003, pp. 704–711 (2003)
Fernadez-Tobis, I., Cantador, I., Kaminskas, M., Ricci, F.: Cross-domain recommender systems: A survey of the state of the art. In: Proceedings of the 2nd Spanish Conference on Information Retrieval (2012)
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)
Pan, W., Xiang, E., Liu, N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: Proceedings of the 24rd AAAI Conference on Artificial Intelligence, pp. 425–434 (2010)
Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain recommender systems. In: Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops, ICDMW 2011, pp. 496–503 (2011)
Tang, J., Wu, S., Sun, J., Su, H.: Cross-domain collaboration recommendation. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1285–1293 (2012)
Winoto, P., Tang, T.Y.: 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 Comput., 209–225 (2008)
Pan, S., Yang, Q.: A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22(10), 1345–1359 (2010)
Xue, G., Dai, W., Yang, Q., Yu, Y.: Topic-bridged plsa for cross-domain text classification. In: SIGIR, pp. 627–634 (2008)
Shi, Y., Larson, M., Hanjalic, A.: Generalized tag-induced cross-domain collaborative filtering. CoRR, abs/1302.4888 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J. (2013). Cross-Domain Recommendation via Cluster-Level Latent Factor Model. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40991-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-40991-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40990-5
Online ISBN: 978-3-642-40991-2
eBook Packages: Computer ScienceComputer Science (R0)