Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
WWW '03 Proceedings of the 12th international conference on World Wide Web
Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System
User Modeling and User-Adapted Interaction
Ontology-based personalized search and browsing
Web Intelligence and Agent Systems
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Semantic annotation, indexing, and retrieval
Web Semantics: Science, Services and Agents on the World Wide Web
An ontology-based information retrieval model
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Semantic annotation of images and videos for multimedia analysis
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Personalized information retrieval based on context and ontological knowledge
The Knowledge Engineering Review
Hybrid query processing for personalized information retrieval on the Semantic Web
Knowledge-Based Systems
Probabilistic score normalization for rank aggregation
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
An online framework for supporting the evaluation of personalised information retrieval systems
iUBICOM'11 Proceedings of the 6th international conference on Ubiquitous and Collaborative Computing
Adapting domain ontology for personalized knowledge search and recommendation
Information and Management
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Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied.