TUP-RS: Temporal User Profile Based Recommender System

  • Wanling ZengEmail author
  • Yang Du
  • Dingqian Zhang
  • Zhili Ye
  • Zhumei Dou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)


As e-commerce continues to emerge in recent years, online stores compete intensely to improve the quality of recommender systems. However, most existing recommender systems failed to consider both long-term and short-term preferences of users based on purchase behavior patterns, ignoring the fact that requirements of users are dynamic. To this end, we present TUP-RS (Temporal User Profile based Recommender System) in this paper. Specifically, the contributions of this paper are two folds: (i) the long-term and short-term preferences from the topic model are combined to construct the temporal user profiles; (ii) the co-training method which shares the parameters in the same feature space is employed to increase the accuracy. We study a subset of data from Amazon and demonstrate that TUP-RS outperforms state-of-the-art methods. Moreover, our recommendation lists are time-sensitive.


Recommender system Topic model Temporal user profile 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Wanling Zeng
    • 1
    • 2
    Email author
  • Yang Du
    • 1
    • 2
  • Dingqian Zhang
    • 1
    • 2
  • Zhili Ye
    • 1
    • 2
  • Zhumei Dou
    • 1
  1. 1.Institute of Software Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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