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The CF+TF-IDF TV-Program Recommendation

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

Abstract

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.

Keywords

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
  • 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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