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| Titel: |
Applying probabilistic latent semantic analysis to multi-criteria recommender system |
| Auteur: |
Zhang, Yin Zhuang, Yueting Wu, Jiangqin Zhang, Liang |
| Verschenen in: |
AI communications |
| Paginering: | Jaargang 22 (2009) nr. 2 pagina's 97-107 |
| Jaar: |
2009-06-15 |
| Inhoud: |
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. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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