The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 6th international conference on Intelligent user interfaces
Information Retrieval
Exploiting Structure for Intelligent Web Search
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 4 - Volume 4
Users want more sophisticated search assistants: results of a task-based evaluation
Journal of the American Society for Information Science and Technology
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The explosive growth of information on the World Wide Web demands effective intelligent search and filtering methods. Consequently, techniques have been developed that extract conceptual information from documents to build domain models automatically. The model we build is a taxonomy of conceptual terms that is used in a search assistant to help the user navigate to the right set of required documents. We monitor the dialogue steps performed by users to get feedback about the quality of choices proposed by the system and to adjust the model without manual intervention. Thus, we employ implicit relevance feedback to improve the domain model. Unlike in traditional relevance feedback and collaborative filtering tasks we do not need explicitly expressed user opinions. Moreover, we aim at improving the domain model as a whole rather than trying to build individual user profiles.