Abstract
The explosive growth of information on theWorldWid eWeb 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 usedin a search assistant to help the user navigate to the right set of requiredd ocuments. We monitor the dialogue steps performed by users to get feedback about the quality of choices proposedb y the system andto adjust the model without manual intervention. Thus, we employ implicit relevance feedback to improve the domain model. Unlike in traditional relevance feedback andcollab orative filtering tasks we do not need explicitly expresseduser opinions. Moreover, we aim at improving the domain model as a whole rather than trying to build individual user profiles.
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Fasli, M., Kruschwitz, U. (2001). Using Implicit Relevance Feedback in a Web Search Assistant. In: Zhong, N., Yao, Y., Liu, J., Ohsuga, S. (eds) Web Intelligence: Research and Development. WI 2001. Lecture Notes in Computer Science(), vol 2198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45490-X_43
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DOI: https://doi.org/10.1007/3-540-45490-X_43
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