Intelligent Multimedia Recommender by Integrating Annotation and Association Mining

Making a decision among a set of items from compound and complex information has been becoming a difficult task for common users. Collaborative filtering has been the mainstay of automatically personalized search employed in contemporary recommender systems. Until now, it is still a challenging issue to reduce the gap between user perception and multimedia contents. To bridge user´s interests and multimedia items, in this paper, we present an intelligent multimedia recommender system by integrating annotation and association mining techniques. In our proposed system, low-level multimedia contents are conceptualized to support rule-based collaborative filtering recommendation by automated annotation. From the discovered relations between user contents and conceptualized multimedia contents, the proposed recommender system can provide a suitable recommendation list to assist users in making a decision among a massive amount of items.