Applying probabilistic latent semantic analysis to multi-criteria recommender system

作者：Zhang Yin^*; Zhuang Yueting; Wu Jiangqin; Zhang Liang

来源：AI Communications, 2009, 22(2): 97-107.

DOI：10.3233/AIC-2009-0446

• Collaborative filtering
• multi-criteria
• latent semantic analysis
• linear Gaussian regression

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.

• 出版日期2009
• 单位浙江大学