Enrichment of Multi-criteria Communities for Context-aware Recommendations

Recommender systems are designed to help users alleviate the information overload problem by offering personalized recommendations. Most systems apply collaborative filtering to predict individual preferences based on opinions of like-minded people through their ratings on items. Recently, context-aware recommender systems (CARSs) are developed to offer users more suitable recommendations by exploiting additional context data such as time, location, etc. However, most CARSs use only ratings as a criterion for building communities, and ignore other available data allowing users to be grouped into communities. This paper presents a novel approach for exploiting multi-criteria communities to provide context-aware recommendations. The main idea of the proposed algorithm is that for a given context, the significance of multi-criteria communities could be different. So communities from the most suitable criteria followed by a learning phase are incorporated into the recommendation process.