Abstract
This work addresses the problem of completing a partially filled matrix incorporating metadata associated with the rows and columns. The basic operation of matrix completion is modeled via deep matrix factorization, and the metadata associations are modeled as graphs. The problem is formally modeled as deep matrix factorization regularized by multiple graph Laplacians. The practical problem of collaborative filtering is an ideal candidate for the proposed solution. It needs to predict missing ratings between users and items, given demographic data of users and metadata associated with items. We show that the proposed solution improves over the state-of-the-art in collaborative filtering.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Agarwal, P., Verma, R., Majumdar, A.: Indian regional movie dataset for recommender systems. arXiv preprint arXiv:1801.02203 (2018)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl.-based Syst. 46, 109–132 (2013)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. arXiv preprint arXiv:1301.7363 (2013)
Candès, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix completion. IEEE Trans. Inf. Theory 56(5), 2053–2080 (2010)
Gu, Q., Zhou, J., Ding, C.: Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs. In: Proceedings of the 2010 SIAM International Conference on Data Mining, pp. 199–210. SIAM (2010)
Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 89–115 (2004)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 77–118. Springer, Boston (2015). https://doi.org/10.1007/978-1-4899-7637-6_3
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Li, T., Ma, Y., Xu, J., Stenger, B., Liu, C., Hirate, Y.: Deep heterogeneous autoencoders for collaborative filtering. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1164–1169. IEEE (2018)
Mongia, A., Jhamb, N., Chouzenoux, E., Majumdar, A.: Deep latent factor model for collaborative filtering. Sig. Process. 169, 107366 (2020)
Mongia, A., Majumdar, A.: Matrix completion on multiple graphs: application in collaborative filtering. Sig. Process. 165, 144–148 (2019)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)
Shamir, O., Shalev-Shwartz, S.: Collaborative filtering with the trace norm: learning, bounding, and transducing. In: Proceedings of the 24th Annual Conference on Learning Theory, pp. 661–678 (2011)
Shi, C., et al.: Deep collaborative filtering with multi-aspect information in heterogeneous networks. IEEE Trans. Knowl. Data Eng. (2019)
Shi, X., Yu, P.S.: Limitations of matrix completion via trace norm minimization. ACM SIGKDD Explor. Newsl. 12(2), 16–20 (2011)
Sun, Y., Babu, P., Palomar, D.P.: Majorization-minimization algorithms in signal processing, communications, and machine learning. IEEE Trans. Sig. Process. 65(3), 794–816 (2016)
Trigeorgis, G., Bousmalis, K., Zafeiriou, S., Schuller, B.W.: A deep matrix factorization method for learning attribute representations. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 417–429 (2016)
Wang, J., De Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 501–508 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mongia, A., Jain, V., Majumdar, A. (2020). Deep Matrix Factorization on Graphs: Application to Collaborative Filtering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_86
Download citation
DOI: https://doi.org/10.1007/978-3-030-63820-7_86
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63819-1
Online ISBN: 978-3-030-63820-7
eBook Packages: Computer ScienceComputer Science (R0)