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An Optimization of Collaborative Filtering Personalized Recommendation Algorithm Based on Time Context Information

  • Xian Jin
  • Qin Zheng
  • Lily Sun
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 449)

Abstract

This paper proposes an improved collaborative filtering algorithm based on time context information. Introducing the time information into the traditional collaborative filtering algorithm, the essay studies the changes of user preference in the time dimension. In this paper the time information includes three aspects: the time context information; the interest decays with the time; items similarity factor. This paper first uses Pearson correlation coefficient calculates time context similarity, pre-filtering the time-context. Through the experiment, the improved algorithm has higher accuracy than the traditional filter algorithms without time factor in the TOP-N recommendation list. It proves that time-context information of user’s can affect the user’s preference.

Keywords

Personalized recommendation collaborative filtering time-context 

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Xian Jin
    • 1
  • Qin Zheng
    • 1
    • 2
  • Lily Sun
    • 3
  1. 1.School of Information Management and EngineeringShanghai University of Finance, and EconomicsShanghaiChina
  2. 2.South University of Science and Technology of ChinaShenzhenChina
  3. 3.School of Systems EngineeringUniversity of ReadingReadingUK

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