Personalized TV recommendation with mixture probabilistic matrix factorization

Huayu Li, Hengshu Zhu, Yong Ge, Yanjie Fu, Yuan Ge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

With the rapid development of smart TV industry, a large number of TV programs have been available for meeting various user interests, which consequently raise a great demand of building personalized TV recommender systems. Indeed, a personalized TV recommender system can greatly help users to obtain their preferred programs and assist TV and channel providers to attract more audiences. While different methods have been proposed for TV recommendations, most of them neglect the mixture of watching groups behind an individual TV. In other words, there may be different groups of audiences at different times in front of a TV. For instance, watching groups of a TV may consist of children, wife and husband, husband, wife, etc in many US household. To this end, in this paper, we propose a Mixture Probabilistic Matrix Factorization (mPMF) model to learn the program preferences of televisions, which assumes that the preference of a given television can be regarded as the mixed preference of different watching groups. Specifically, the latent vector of a television is drawn from a mixture of Gaussian and the mixture number is the estimated number of watching groups behind the television. To evaluate the proposed mPMF model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on a real-world data set. The experimental residís clearly demonstrate the effectiveness of our model.

Original languageEnglish (US)
Title of host publicationSIAM International Conference on Data Mining 2015, SDM 2015
EditorsSuresh Venkatasubramanian, Jieping Ye
PublisherSociety for Industrial and Applied Mathematics Publications
Pages352-360
Number of pages9
ISBN (Electronic)9781510811522
DOIs
StatePublished - 2015
Externally publishedYes
EventSIAM International Conference on Data Mining 2015, SDM 2015 - Vancouver, Canada
Duration: Apr 30 2015May 2 2015

Publication series

NameSIAM International Conference on Data Mining 2015, SDM 2015

Conference

ConferenceSIAM International Conference on Data Mining 2015, SDM 2015
Country/TerritoryCanada
CityVancouver
Period4/30/155/2/15

Keywords

  • Mixture probabilistic matrix factorization
  • Recommender systems
  • Smart TV

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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