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.