Multimedia data is known for its variety and also for the difficulty that comes in extracting relevant features from multimedia data. Owing to which the collaborative recommendation systems have found their foothold in multimedia recommender systems. However, modern-day multimedia sites have tons of user history in the form of user feedback, reviews, votes, comments, and etc. We can use these social interactions to extract useful content features, which can then be used in content based recommendation system. In this paper, we propose a novel hybrid recommender system that combines the content and collaborative systems using a Bayesian model. We substitute the concrete textual content with a sparse tag information. Extensive experiments on real-world dataset show that tags significantly improves the recommendation performance for multimedia data.
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