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Adaptive Temporal Model for IPTV Recommendation

  • Yan Yang
  • Qinmin HuEmail author
  • Liang He
  • Minjie Ni
  • Zhijin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9098)

Abstract

How to help the IPTV service provider make the program recommendation to their clients is the problem we propose to solve in this paper. Here we offer an adaptive temporal model to identify multiple members under a shared IPTV account. The time intervals are first detected and defined in each account. Then, the preference similarity is calculated among the intervals to extract the members. After that, we evaluate our model on the industrial data sets by a famous IPTV provider. The experimental results show that our proposed model is promising and outperform the state-of-the-art algorithms with low computational complexity and versatility without user feedback. Furthermore, the proposed model has been officially adopted by the IPTV provider and applied in their IPTV systems with excellent user satisfaction in 2013.

Keywords

Recommender System Subspace Cluster Personalized Recommendation IPTV Service Program Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yan Yang
    • 1
  • Qinmin Hu
    • 1
    Email author
  • Liang He
    • 1
  • Minjie Ni
    • 1
  • Zhijin Wang
    • 1
  1. 1.Shanghai Key Laboratory of Multidimensional Information Processing, Department of Computer Science and TechnologyEast China Normal University ShanghaiShanghaiChina

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