Personalized TV Recommendation with Mixture Probabilistic Matrix Factorization
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 results clearly demonstrate the effectiveness of our model.
H. Li et al., "Personalized TV Recommendation with Mixture Probabilistic Matrix Factorization," Proceedings of the 2015 SIAM International Conference on Data Mining (2015, Vancouver, Canada), pp. 352-360, Society for Industrial and Applied Mathematics Publications, Apr 2015.
The definitive version is available at https://doi.org/10.1137/1.9781611974010.40
2015 SIAM International Conference on Data Mining, SDM 2015 (2015: Apr. 30-May 2, Vancouver, Canada)
Keywords and Phrases
Data mining; Factorization; Mixtures; Recommender systems; Space division multiple access; Virtual reality; Baseline methods; Evaluation metrics; Mixture of Gaussians; Personalized TV; Probabilistic matrix factorizations; Smart-TV; State of the art; User interests; Matrix algebra; Mixture probabilistic matrix factorization; Recommender systems; Smart TV
International Standard Book Number (ISBN)
Article - Conference proceedings
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