1. Applying probabilistic latent semantic analysis to multi-criteria recommender system.
- Author
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Yin Zhang, Yueting Zhuang, Jiangqin Wu, and Liang Zhang
- Subjects
- *
STATISTICS , *LEARNING , *PROBABILITY theory , *ALGORITHMS , *GAUSSIAN distribution , *REGRESSION analysis - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2009
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