Improving retrieval performance by relevance feedback
Readings in information retrieval
Dependence language model for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Query expansion using term relationships in language models for information retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Can social bookmarking enhance search in the web?
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Towards better understanding of folksonomic patterns
Proceedings of the eighteenth conference on Hypertext and hypermedia
Social semantic query expansion
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Social networks and collaborative tagging systems are rapidly gaining popularity as primary means for sorting and sharing data: users tag their bookmarks in order to simplify information dissemination and later lookup. Social Bookmarking services are useful in two important respects: first, they can allow an individual to remember the visited URLs, and second, tags can be made by the community to guide users towards valuable content. In this paper we focus on the latter use: we present a novel approach for personalized web search using query expansion. We further extend the family of well-known co-occurence matrix technique models by using a new way of exploring social tagging services. Our approach shows its strength particularly in the case of disambiguation of word contexts. We show how to design and implement such a system in practice and conduct several experiments. To the best of our knowledge this is the first study centered on using social bookmarking and tagging techniques for personalization of web search and its evaluation in a real-world scenario.