Abstract
Clustering techniques have been proved effective to deal with the sparsity and scalability problems in collaborative filtering recommender systems. They aim to identify groups of users having similar preferences or items sharing similar topics. In this study, we propose an integrated recommendation framework based on matrix factorization. Firstly, users and items are clustered into multiple groups and a pair of strongly related user group and item group forms a submatrix. Then some traditional collaborative filtering technique is executed in every submatrix. The final rating predictions are generated by aggregating results from all the submatrices and the items are recommended with a Top-N strategy. Experimental results show that the proposed framework significantly improves the recommendation accuracy of several state-of-the-art collaborative filtering methods, while retains the advantage of good scalability.
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
G. Guo, J. Zhang, N. Yorke-Smith, Leveraging multiviews of trust and similarity to enhance clustering-based recommender systems. Knowl.-Based Syst. 74(1), 14–27 (2015)
B. Li, Q. Yang, X. Xue, Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction, in Proceedings of the 21st International Jont Conference on Artifical Intelligence, Pasadena, California, USA, pp. 2052–2057, 2009
Z. Huang, D. Zeng, H. Chen et al., A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intell. Syst. 22(5), 68–78 (2007)
Z. Li, X. Wu, Weighted nonnegative matrix tri-factorization for co-clustering, in 23rd IEEE International Conference on Tools with Artificial Intelligence, Boca Raton, FL, USA, pp. 811–816 (2011)
B. Xu, J. Bu, C. Chen et al., An exploration of improving collaborative recommender systems via user-item subgroups, in Proceedings of the 21st International Conference on World Wide Web, Lyon, France, pp. 21–30 (2012)
J. Liu, Y. Jiang, Z. Li et al., Domain-sensitive recommendation with user-item subgroup analysis. IEEE Trans. Knowl. Data Eng. 28(4), 939–950 (2016)
Y. Zhang, M. Zhang, Y. Liu et al., Improve collaborative filtering through bordered block diagonal form matrices, in International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 313–322 (2013)
C. Ding, X. He, H.D. Simon et al., On the equivalence of nonnegative matrix factorization and spectral clustering, in Proceedings of the SIAM International Conference on Data Mining, pp. 606–610 (2005)
Q. Gu, J. Zhou, C. Ding et al., Collaborative filtering: weighted nonnegative matrix factorization incorporating user and item graphs, in Proceedings of the SIAM International Conference on Data Mining, pp. 199–210 (2010)
G. Chen, F. Wang, C. Zhang et al., Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Inf. Process. Manage. 45(3), 368–379 (2009)
H. Langseth, T.D. Nielsen, Scalable learning of probabilistic latent models for collaborative filtering. Decis. Support Syst. 74, 1–11 (2015)
B. Sarwar, G. Karypis, J. Konstan et al., Application of dimensionality reduction in recommender system—a case study, in ACM Webkdd Workshop (2000)
H. Shan, A. Banerjee, Generalized probabilistic matrix factorizations for collaborative filtering, in IEEE 10th International Conference on Data Mining, Sydney, NSW, Australia, pp. 1025–1030 (2010)
J.S. Breese, D. Heckerman, C.M. Kadie et al., Empirical analysis of predictive algorithms for collaborative filtering, in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Madison, Wisconsin, USA, pp. 43–52 (1998)
Z. Kang, C. Peng, Q. Cheng, Top-N recommender system via matrix completion, in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp. 179–184 (2016)
P. Cremonesi, Y. Koren, R. Turrin, Performance of recommender algorithms on top-n recommendation tasks, in Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain, pp. 39–46 (2010)
Acknowledgements
The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71371135).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Tian, J., Qu, Y. (2019). A Novel Framework for Top-N Recommendation Based on Non-negative Matrix Tri-Factorization. In: Huang, G., Chien, CF., Dou, R. (eds) Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-3402-3_36
Download citation
DOI: https://doi.org/10.1007/978-981-13-3402-3_36
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3401-6
Online ISBN: 978-981-13-3402-3
eBook Packages: Business and ManagementBusiness and Management (R0)