A user attention model for video summarization
Proceedings of the tenth ACM international conference on Multimedia
A Movie Recommendation System—An Application of Voting Theory in User Modeling
User Modeling and User-Adapted Interaction
Personalized multimedia retrieval: the new trend?
Proceedings of the international workshop on Workshop on multimedia information retrieval
A Graphical Model for Context-Aware Visual Content Recommendation
IEEE Transactions on Multimedia
A retrieval method adaptively reducing user's subjective impression gap
Multimedia Tools and Applications
You are what you consume: a bayesian method for personalized recommendations
Proceedings of the 7th ACM conference on Recommender systems
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Facing the vast amount of novel production in the movie industry, people are in favor of choosing their favorite candidates quickly and previewing movie contents conveniently so as to decide whether they appeal to their personal taste. To meet this growing need, researchers are paying more attention on Personalization and Recommendation, the new trends of multimedia information retrieval, by integrating content and contextual information. In this paper, we propose a hierarchical framework for personalized movie recommendation. First, movie weekly ranking information is utilized for movie association and recommendation. Then, an integrated graph with both movie content and user preference is constructed to generate dynamic movie synopsis for personalized navigation. The superiorities of the proposed method have two aspects: 1) The prior knowledge independent recommendation scheme is implemented to replace the traditional ranking method for novel information access; 2) Personalized movie synopsis is interactively produced to replace the current movie trailer for preview. The promising results of subjective evaluation indicate that the proposed framework can discover the latent relationship between movies as well as movie highlights and therefore provide personalized movie recommendation to effectively lead movie access in an individualized manner.