Abstract
In a recommendation system, user preference patterns and the preference dynamic effect are observed in the user ×item rating matrix. However, their value has barely been exploited in previous research. In this paper, we formalize the preference pattern as a sparse matrix and propose a Preference Pattern Subspace to iteratively model the personal and the global preference patterns with an EM-like algorithm. Furthermore, we propose a PrepSVD-I algorithm by transforming the Top-N recommendation as a pairwise preference learning process. Experiment results show that the proposed PrepSVD-I algorithm significantly outperforms the state-of-the-art Top-N recommendation algorithms.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abramowitz, M., Stegun, I.A.: Handbook of Mathematical Functions: with Formulas, Graphs, and Mathematical Tables. Dover, New York (1972)
Anderson, C.: The Long Tail: Why the Future of Business Is Selling Less of More, vol. 33. Hyperion (2006)
Cremonesi, P., Garzotto, F., Negro, S., Papadopoulos, A.: Comparative Evaluation of Recommender System Quality. In: CHI Extended Abstracts, pp. 1927–1932 (2011)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of Recommender Algorithms on Top-N Recommendation Tasks. In: Recsys, pp. 39–46. ACM (2010)
Deshpande, M., Karypis, G.: Item-based top- N recommendation algorithms. ACM TOIS 22(1), 143–177 (2004)
Geng, X., Zhou, Z.-H., Smith-Miles, K.: Automatic age estimation based on facial aging patterns. IEEE TPAMI 29(12), 2234–2240 (2007)
Golub, G.H., Van Loan, C.F.: Introduction to Matrix, 3rd edn., vol. 1. The Johns Hopkins University Press, Baltimore (1996)
Herbrich, R., Graepel, T., Obermayer, K.: Support Vector Learning for Ordinal Regression. In: ICANN 1999, vol. 470, pp. 97–102 (1999)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: CIKM 2001, pp. 247–254. ACM (2001)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD 2008, pp. 426–434. ACM (2008)
Koren, Y.: Collaborative filtering with temporal dynamics. In: SIGKDD 2009, pp. 447–456. ACM (2009)
Ning, X., Karypis, G.: SLIM: Sparse Linear Methods for Top-N Recommender Systems. In: ICDM 2011, pp. 497 – 506 (2011)
Xiong, L., Chen, X., Huang, T.K., Schneider, J., Carbonell, J.G.: Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: Proceedings of SIAM Data Mining (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ren, Y., Zhu, T., Li, G., Zhou, W. (2013). Top-N Recommendations by Learning User Preference Dynamics. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-37456-2_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37455-5
Online ISBN: 978-3-642-37456-2
eBook Packages: Computer ScienceComputer Science (R0)