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      Learning to Rank for Expert Search in Digital Libraries of Academic Publications

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          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.

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          Training linear SVMs in linear time

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            Generalized Hirsch h-index for disclosing latent facts in citation networks

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              A support vector method for optimizing average precision

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                Journal
                1302.0413

                Information & Library science
                Information & Library science

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