Abstract
In recommender systems, personal characteristic is possessed by not only users but also displaying products. Users have their personal rating patterns while products have different characteristics that attract users. This information can be explicitly exploited from the review text. However, most existing methods only model the review text as a topic preference of products, without considering the perspectives of users and products simultaneously. In this paper, we propose a user-product topic model to capture both user preferences and attractive characteristics of products. Different from conventional collaborative filtering in conjunction with topic models, we use non-negative matrix tri-factorization to jointly reveal the characteristic of users and products. Experiments on two real-world data sets validate the effectiveness of our method in Top-N recommendations.
The first two authors contributed equally to this work which was finished when Xiangsheng Li was an undergraduate student of his final year.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Chen, C., Zheng, X., Wang, Y., Hong, F., Lin, Z.: Context-ware collaborative topic regression with social matrix factorization for recommender systems. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 9–15 (2014)
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 448–456 (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Yang, X., Huang, K., Zhang, R., Hussain, A.: Learning latent features with infinite non-negative binary matrix tri-factorization. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9947, pp. 587–596. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46687-3_65
Li, T., Ding, C.: The relationships among various nonnegative matrix factorization methods for clustering. In: Proceedings of the 6th International Conference on Data Mining, pp. 362–371 (2006)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Proceedings of Advances in Neural Information Processing Systems, pp. 1257–1264 (2007)
Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Ind. Inform. 10, 1273–1284 (2014)
Hernando, A., Bobadilla, J., Ortega, F.: A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowl.-Based Syst. 97, 188–202 (2016)
Zhang, S., Wang, W., Ford, J., Makedon, F.: Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM International Conference on Data Mining, pp. 548–552 (2006)
Guillamet, D., Vitrià, J., Schiele, B.: Introducing a weighted non-negative matrix factorization for image classification. Pattern Recogn. Lett. 24, 2447–2454 (2003)
Ding, C., Li, T., Peng, W., Park, H.: Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 126–135 (2006)
Kang, Z., Peng, C., Cheng, Q.: Top-N recommender system via matrix completion. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 179–185 (2016)
Bennett, J., Elkan, C., Liu, B., Smyth, P., Tikk, D.: KDD Cup and workshop 2007. SIGKDD Explor. 9, 51–52 (2007)
McAuley, J., Leskovec, J.: Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 165–172 (2013)
Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Proceedings of Advances in Neural Information Processing Systems, pp. 556–562 (2001)
Bertsekas, D.P.: Nonlinear Programming. Athena Scientific, Belmont (1999)
Diao, Q., Qiu, M., Wu, C., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 193–202 (2014)
Acknowledgements
We are grateful to the anonymous reviewers for their valuable comments on this manuscript. The research has been supported by the National Natural Science Foundation of China (61502545, U1611264, U1711262), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), and the Individual Research Scheme of the Dean’s Research Fund 2017–2018 (FLASS/DRF/IRS-8) of The Education University of Hong Kong.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Li, X. et al. (2018). Learning Dual Preferences with Non-negative Matrix Tri-Factorization for Top-N Recommender System. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-91452-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91451-0
Online ISBN: 978-3-319-91452-7
eBook Packages: Computer ScienceComputer Science (R0)