Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
A Hybrid Approach to Making Recommendations and Its Application to the Movie Domain
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
A Hybrid Movie Recommender System Based on Neural Networks
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
An Ontology-Based Product Recommender System for B2B Marketplaces
International Journal of Electronic Commerce
A recommender system framework combining neural networks & collaborative filtering
IMCAS'06 Proceedings of the 5th WSEAS international conference on Instrumentation, measurement, circuits and systems
An ontology-based personalized target advertisement system on interactive TV
Multimedia Tools and Applications
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In order to make recommendations to a user, a recommender mainly uses two approaches: content-basedfiltering approach and collaborative filtering approach. However, they both still have some shortcomings technically. The content-based approach is difficult to handle feature extraction as well as user intension prediction. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. In this paper, we present an novel hybrid recommendation approach based on Ontology and Neural Network in the movie domain. The approach combines content-based filtering and collaborativefiltering and a recommender can use them individually or use them both. The hybrid recommendation approach can tackle the traditional recommenders -- problems, such as feature extraction, intension prediction, matrix sparsity and cold start problems. Our experiments show that, our approach provides a good method to make recommendations to users.