1. Applying probabilistic latent semantic analysis to multi-criteria recommender system
- Author
-
Liang Zhang, Yin Zhang, Yueting Zhuang, and Jiangqin Wu
- Subjects
Probabilistic latent semantic analysis ,Latent semantic analysis ,Computer science ,business.industry ,Gaussian ,Bayesian network ,Regression analysis ,Recommender system ,Machine learning ,computer.software_genre ,Latent class model ,symbols.namesake ,Artificial Intelligence ,Collaborative filtering ,symbols ,Artificial intelligence ,Data mining ,business ,computer - Abstract
Nowadays some recommender system researchers have already been engaging multi-criteria that model possible attributes of the item to generate the improved recommendations. However, the statistical machine learning methods successful in the single-rating recommender system have not been investigated in the context of multi-criteria ratings. In this paper, we propose two types of multi-criteria probabilistic latent semantic analysis algorithms extended from the single-rating version. First, the mixture of multi-variate Gaussian distribution is assumed to be the underlying distribution of multi-criteria ratings of each user. Second, we further assume the mixture of the linear Gaussian regression model as the underlying distribution of multi-criteria ratings of each user, inspired by the Bayesian network and linear regression. The experiment results on the Yahoo!Movies ratings data set show that the full multi-variate Gaussian model and the linear Gaussian regression model achieve a stable performance gain over other tested methods.
- Published
- 2009