Abstract
The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence in an optimal way. This paper explores the usage of learning to rank methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure with the citation patterns for the community of experts, and from profile information about the experts. Experiments made over a dataset of academic publications, for the area of Computer Science, attest for the adequacy of the proposed approaches.
This work was partially supported by the Fundação para a Ciência e Tecnologia (FCT), through project grant PTDC/EIA-EIA/115346/2009 (SMARTIES), and by the ICP Competitiveness and Innovation Framework Program of the European Commission, through the European Digital Mathematics Library (EuDML) project – http://www.eudml.eu/
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Moreira, C., Calado, P., Martins, B. (2011). Learning to Rank for Expert Search in Digital Libraries of Academic Publications. In: Antunes, L., Pinto, H.S. (eds) Progress in Artificial Intelligence. EPIA 2011. Lecture Notes in Computer Science(), vol 7026. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24769-9_32
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DOI: https://doi.org/10.1007/978-3-642-24769-9_32
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