Context-aware recommender systems that provide better recommendations for users by using their rating history in different situations have been proposed. Because incorporating all contextual information can make the data sparser and degrade the prediction accuracy, most context-aware methods focus on detecting and using only the most effective contextual factors. However, in addition to accuracy, the diversity of the recommendation is also a key to improving users' satisfaction with recommendation results. Moreover, most context-aware techniques have not considered directly the relationships among context, users, and items before predicting the ratings. In the real world, different contextual factors tend to affect users and items differently. This paper proposes a latent probabilistic model to incorporate the contextual information. By adopting a binary particle-swarm optimization technique, the relevant contextual factors for user classes and item classes are identified and incorporated into the model. We optimize our model for two cases, namely considering accuracy alone and considering the trade-off between accuracy and diversity. An evaluation shows that our proposed model performs better than 1) a model that considers only the relation of context to users alone or items alone, 2) a model that exploits all contextual factors, and 3) the traditional context-aware recommendation method.
['Padipat Sitkrongwong', 'Saranya Maneeroj', 'Pannawit Samatthiyadikun', 'Atsuhiro Takasu']
Bayesian probabilistic model for context-aware recommendations