Abstract
Cross-domain recommender systems represent an emerging research topic as users generally have interactions with items from multiple domains. One goal of a cross-domain recommender system is to improve the quality of recommendations in a target domain by using user preference information from other source domains. We observe that, in many applications, users interact with items of different types (e.g., artists and tags). Each recommendation problem, for example, recommending artists or recommending tags, can be seen as a different task, or, in general, a different domain. Furthermore, for such applications, explicit feedback may not be available, while implicit feedback is readily available. To handle such applications, in this paper, we propose a novel cross-domain collaborative filtering approach, based on a regularized latent factor model, to transfer knowledge between source and target domains with implicit feedback. More specifically, we identify latent user and item factors in the source domains, and transfer the user factors to the target, while controlling the amount of knowledge transferred through regularization parameters. Experimental results on six target recommendation tasks (or domains) from two real-world applications show the effectiveness of our approach in improving target recommendation accuracy as compared to state-of-the-art single-domain collaborative filtering approaches. Furthermore, preliminary results also suggest that our approach can handle varying percentages of user overlap between source and target domains.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
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 RecSys (2007)
Cantador, I., Brusilovsky, P., Kuflik, T.: 2nd workshop on information heterogeneity and fusion in recommender systems (hetrec 2011). In: Proceedings of RecSys (2011)
Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain recommender systems. In: Proceedings of ICDMW (2011)
Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part II. LNCS, vol. 8189, pp. 161–176. Springer, Heidelberg (2013)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)
Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., Zhu, C.: Personalized recommendation via cross-domain triadic factorization. In: Proceedings of WWW (2013)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of ICDM (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Li, B., Yang, Q., Xue, X.: Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction. In: Proceedings of IJCAI (2009)
Li, B., Yang, Q., Xue, X.: Transfer learning for collaborative filtering via a rating-matrix generative model. In: Proceedings of ICML (2009)
Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: Proceedings of AAAI (2010)
Parimi, R., Caragea, D.: Leveraging multiple networks for author personalization. In: Scholarly Big Data, AAAI Workshop (2015)
Said, A., BellogÃn, A.: Comparative recommender system evaluation: benchmarking recommendation frameworks. In: Proceedings of RecSys (2014)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW (2001)
Shapira, B., Rokach, L., Freilikhman, S.: Facebook single and cross domain data for recommendation systems. User Model. User-Adap. Interact. 23, 211–247 (2013)
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of ACM SIGKDD (2008)
Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., Su, Z.: Arnetminer: extraction and mining of academic social networks. In: Proceedings of KDD (2008)
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 Gener. Comput. 26(3), 209–225 (2008)
Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the netflix prize. In: Fleischer, R., Xu, J. (eds.) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008)
Acknowledgements
The computing for this project was performed on the Beocat Research Cluster at Kansas State University, which is funded, in part, by grants MRI-1126709, CC-NIE-1341026, MRI-1429316, CC-IIE-1440548.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Parimi, R., Caragea, D. (2015). Cross-Domain Matrix Factorization for Multiple Implicit-Feedback Domains. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-27926-8_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27925-1
Online ISBN: 978-3-319-27926-8
eBook Packages: Computer ScienceComputer Science (R0)