Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Concept features in Re:Agent, an intelligent Email agent
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
OIL: An Ontology Infrastructure for the Semantic Web
IEEE Intelligent Systems
Exploiting hierarchical domain structure to compute similarity
ACM Transactions on Information Systems (TOIS)
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
IEEE Transactions on Knowledge and Data Engineering
ATLAS: a framework to provide multiuser and distributed t-learning services over MHP
Software—Practice & Experience - Research Articles
SXRS: An XLink-based Recommender System using Semantic Web technologies
Expert Systems with Applications: An International Journal
Syskill & webert: Identifying interesting web sites
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Ontology-based semantic recommendation for context-aware e-learning
UIC'07 Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Expert Systems with Applications: An International Journal
Context semantic filtering for mobile advertisement
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems
A web search-centric approach to recommender systems with URLs as minimal user contexts
Journal of Systems and Software
Personalization and content awareness in online lab - virtual computational laboratory
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Engineering Applications of Artificial Intelligence
Keyword clustering for user interest profiling refinement within paper recommender systems
Journal of Systems and Software
Semantic inference of user's reputation and expertise to improve collaborative recommendations
Expert Systems with Applications: An International Journal
Bringing knowledge into recommender systems
Journal of Systems and Software
Recommendation algorithm of the app store by using semantic relations between apps
The Journal of Supercomputing
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Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.