Abstract
In this work, we present a method to characterize a given topic on an Information Retrieval System based on expert user profiles. We start from a set of documents from which a set of characteristic terms is extracted. The presence of any term in each document is known and we want to establish the most significant ones in order to select relevant documents about a given topic ∏. For that purpose, a group of experts are required to assess the set of documents. The experts can query with the same terms (an unique query) to the system or with different terms (several queries). By aggregating these assessments with the weight associated to the terms, a topic profile can be obtained. An overview of these different situations and an experimental example are also presented.
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Delgado, M., Martín-Bautista, M.J., Sánchez, D., Serrano, J.M., Vila, MA. (2003). Building Topic Profiles Based on Expert Profile Aggregation. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds) Advances in Web Intelligence. AWIC 2003. Lecture Notes in Computer Science, vol 2663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44831-4_24
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DOI: https://doi.org/10.1007/3-540-44831-4_24
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