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.
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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
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DOI: https://doi.org/10.1007/978-3-642-37456-2_33
Publisher Name: Springer, Berlin, Heidelberg
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