TY - GEN
T1 - Personalized TV recommendation with mixture probabilistic matrix factorization
AU - Li, Huayu
AU - Zhu, Hengshu
AU - Ge, Yong
AU - Fu, Yanjie
AU - Ge, Yuan
N1 - Funding Information:
Acknowledgements: This research was supported in part by National Institutes of Health under Grant 1R21AA023975-0T and National Natural Science Foundation of China under Grant 61203034.
Publisher Copyright:
Copyright © SIAM.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Mixture probabilistic matrix factorization
KW - Recommender systems
KW - Smart TV
UR - http://www.scopus.com/inward/record.url?scp=84961923784&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961923784&partnerID=8YFLogxK
U2 - 10.1137/1.9781611974010.40
DO - 10.1137/1.9781611974010.40
M3 - Conference contribution
AN - SCOPUS:84961923784
T3 - SIAM International Conference on Data Mining 2015, SDM 2015
SP - 352
EP - 360
BT - SIAM International Conference on Data Mining 2015, SDM 2015
A2 - Venkatasubramanian, Suresh
A2 - Ye, Jieping
PB - Society for Industrial and Applied Mathematics Publications
T2 - SIAM International Conference on Data Mining 2015, SDM 2015
Y2 - 30 April 2015 through 2 May 2015
ER -