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Top-N Recommendations by Learning User Preference Dynamics

  • Yongli Ren
  • Tianqing Zhu
  • Gang Li
  • Wanlei Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7819)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yongli Ren
    • 1
  • Tianqing Zhu
    • 1
  • Gang Li
    • 1
  • Wanlei Zhou
    • 1
  1. 1.School of Information TechnologyDeakin UniversityAustralia

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