Web metasearch: rank vs. score based rank aggregation methods
Proceedings of the 2003 ACM symposium on Applied computing
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
P-TAG: large scale automatic generation of personalized annotation tags for the web
Proceedings of the 16th international conference on World Wide Web
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Personalized search on the world wide web
The adaptive web
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Folksonomy-boosted social media search and ranking
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Folksonomy-based personalized search and ranking in social media services
Information Systems
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In this paper, we investigate the exploitation of user profiles defined in social tagging services to personalize Web search. One of the key challenges of a personalization framework is the elicitation of user profiles able to represent user interests. We propose a personalization approach that exploits the tagging information of users within a social tagging service as a way of obtaining their interests. We evaluate this approach in Delicious, a social Web bookmarking service, and apply our personalization approach to a Web search system. Our evaluation results indicate a clear improvement of our approach over related state of the art personalization approaches.