Abstract
Collaborative Filtering (CF) is a popular strategy for recommender systems, which infers users’ preferences typically using either explicit feedback (e.g., ratings) or implicit feedback (e.g., clicks). Explicit feedback is more accurate, but the quantity is not sufficient; whereas implicit feedback has an abundant quantity, but can be fairly inaccurate. In this paper, we propose a novel method, Expectation-Maximization Collaborative Filtering (EMCF), based on matrix factorization. The contributions of this paper include: first, we combine explicit and implicit feedback together in EMCF to infer users’ preferences by learning latent factor vectors from matrix factorization; second, we observe four different cases of implicit feedback in terms of the distribution of latent factor vectors, and then propose different methods to estimate implicit feedback for different cases in EMCF; third, we develop an algorithm for EMCF to iteratively propagate the estimations of implicit feedback and update the latent factor vectors in order to fully utilize implicit feedback. We designed experiments to compare EMCF with other CF methods. The experimental results show that EMCF outperforms other methods by combining explicit and implicit feedback.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bell, R.M., Koren, Y.: Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights, Los Alamitos, CA, USA, pp. 43–52 (2007)
Desrosiers, C., Karypis, G.: A Comprehensive Survey of Neighborhood-based Recommendation Methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston (2011)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS) 22, 89–115 (2004)
Hu, Y., Koren, Y., Volinsky, C.: Collaborative Filtering for Implicit Feedback Datasets. In: IEEE International Conference on, Los Alamitos, CA, USA, pp. 263–272 (2008)
Koren, Y., Bell, R., Volinsky, C.: Matrix Factorization Techniques for Recommender Systems. Computer 42(8), 30–37 (2009)
Liu, N.N., Xiang, E.W., Zhao, M., Yang, Q.: Unifying explicit and implicit feedback for collaborative filtering, New York, NY, USA, pp. 1445–1448 (2010)
Pan, R., Scholz, M.: Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD, Paris, France, p. 667 (2009)
Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. In: Advances in Artificial Intelligence (2009)
Wang, J., Robertson, S., Vries, A.P., Reinders, M.J.T.: Probabilistic relevance ranking for collaborative filtering. Information Retrieval 11(6), 477–497 (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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, B., Rahimi, M., Zhou, D., Wang, X. (2012). Expectation-Maximization Collaborative Filtering with Explicit and Implicit Feedback. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30217-6_50
Download citation
DOI: https://doi.org/10.1007/978-3-642-30217-6_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30216-9
Online ISBN: 978-3-642-30217-6
eBook Packages: Computer ScienceComputer Science (R0)