A Context-Aware Method for Top-k Recommendation in Smart TV

  • Peng Liu
  • Jun MaEmail author
  • Yongjin Wang
  • Lintao Ma
  • Shanshan Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9932)


We discuss the video recommendation for smart TV, an increasingly popular media service that provides online videos by TV sets. We propose an effective video recommendation model for smart TV service (RSTV) based on the developed Latent Dirichlet allocation(LDA) to make personalized top-k video recommendation. In addition, we present proper solutions for some critical problems of the smart TV recommender system, such as sparsity problem and contextual computing. Our analysis is conducted using a real world dataset gathered from Hisense smart TV platform, JuHaoKan Video-on-Demand dataset(JHKVoD), which is an implicit watch-log dataset collecting sets of videos watched by each user with their corresponding timestamps. We fully portray our dataset in many respects, and provide details on the experimentation and evaluation framework. Result shows that RSTV performs better comparing to many other baselines. We analyse the influence of some of the parameters as well as the contextual granularity.


Context-aware recommendation system Smart TV 



This work was supported by National Key Laboratory, Hisense Co., Ltd., and Natural Science Foundation of China (61272240, 71402083).


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Peng Liu
    • 1
  • Jun Ma
    • 1
    Email author
  • Yongjin Wang
    • 2
  • Lintao Ma
    • 2
  • Shanshan Huang
    • 2
  1. 1.School of Computer Science and TechnologyShandong UniversityJinanChina
  2. 2.National Key LaboratoryHisense Co., Ltd.QingdaoChina

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