Using Implicit Relevance Feedback in
نویسنده
چکیده
The explosive growth of information on the World Wide Web demands eeective intelligent search and ltering 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 ltering 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 prooles.
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