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Applying probabilistic latent semantic analysis to multi-criteria recommender system.
- Source :
-
AI Communications . 2009, Vol. 22 Issue 2, p97-107. 11p. 3 Diagrams, 1 Chart, 2 Graphs. - Publication Year :
- 2009
-
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]
Details
- Language :
- English
- ISSN :
- 09217126
- Volume :
- 22
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- AI Communications
- Publication Type :
- Academic Journal
- Accession number :
- 41688602
- Full Text :
- https://doi.org/10.3233/AIC-2009-0446