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

  • Catarina Moreira
  • Pável Calado
  • Bruno Martins
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7026)

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.

Keywords

Digital Library Average Precision Query Term Mean Average Precision Ranking Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Catarina Moreira
    • 1
  • Pável Calado
    • 1
  • Bruno Martins
    • 1
  1. 1.Instituto Superior Técnico, INESC-IDPorto SalvoPortugal

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