The CF+TF-IDF TV-Program Recommendation

  • Li Yan
  • Cui JinrongEmail author
  • Xin Liu
  • Yu JiaHao
  • He Mingkai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 849)


This paper first analyzes and discusses the traditional methods used in the TV-program recommendation system. Current studies show that the explicit data methods (user interest preference matrix) used in the real-world datasets does not work well and the precision of implicit data method (collaborative filtering) greatly relies on the data amount of each user. Then, we introduce a new method called CF+TF-IDF, which combines the collaborative filtering and TF-IDF algorithm. In order to analyze users’ preference, we also add k-means++ algorithm in it to cluster the users. The core of the method is to infer users’ preference from their viewing habits and the program type they choose. By using CF+TF-IDF, we build a TV-program recommendation model, aiming at improving users’ viewing experience.


TV-program recommendation Collaborative filtering TF-IDF k-means++ 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Li Yan
    • 1
  • Cui Jinrong
    • 2
    Email author
  • Xin Liu
    • 1
  • Yu JiaHao
    • 1
  • He Mingkai
    • 1
  1. 1.South China Agricultural UniversityGuangzhouChina
  2. 2.College of Mathematics and InformaticsSouth China Agricultural UniversityGuangzhouChina

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