Bringing mobile tv to the mashup approach
Proceedings of the 8th international interactive conference on Interactive TV&Video
Developing an ontology-supported information integration and recommendation system for scholars
Expert Systems with Applications: An International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
An intelligent framework for e-government personalized services
Proceedings of the 14th Annual International Conference on Digital Government Research
A framework for learning and analyzing hybrid recommenders based on heterogeneous semantic data
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Multi-Criteria Recommender Systems based on Multi-Attribute Decision Making
Proceedings of International Conference on Information Integration and Web-based Applications & Services
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Recommender systems arose in view of the information overload present in numerous domains. The so-called content-based recommenders offer products similar to those the users liked in the past. However, due to the use of syntactic similarity metrics, these systems elaborate overspecialized recommendations including products very similar to those the user already knows. In this paper, we present a strategy that overcomes overspecialization by applying reasoning techniques borrowed from the semantic Web. Thanks to the reasoning, our strategy discovers a huge amount of knowledge about the user's preferences, and compares them with available products in a more flexible way, beyond the conventional syntactic metrics. Our reasoning-based strategy has been implemented in a recommender system for interactive digital television, with which we checked that the proposed technique offers accurate enhanced suggestions that would go unnoticed in the traditional approaches.