Ontology-based TV Program Contents Retrieval and Recommendation

In this paper we propose a searching and recommendation method of TV program contents in order to reduce the information overload problem. Most of previous recommendation approaches are dependent on simple ratings of users to determine preference similarity among the users. But our approach is closely related to the combination of ontology-based TV program searching and content-based filtering based on TV ontology and usage history. We search TV programs by computing the similarity between contents ontologies, filter the candidates with preferences of users, and return the ranked list of TV programs. A subjective experiments show that our proposed method is effective in semantic-based searching and recommendation.