Voting for candidates: adapting data fusion techniques for an expert search task

MacDonald, C. and Ounis, I. (2006) Voting for candidates: adapting data fusion techniques for an expert search task. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Arlington, Virginia, USA, 6-11 November 2006, pp. 387-396. ISBN 1595934332 (doi: 10.1145/1183614.1183671)

Abstract

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models.

• Item Type: Conference Proceedings
• Additional Information: © ACM, 2006. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 15th ACM International Conference on Information and Knowledge Management, (2006) http://doi.acm.org/10.1145/1183614.1183671
• Keywords: Voting, expert finding, expertise modelling, expert search information retrieval, ranking, data fusion.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Macdonald, Professor Craig and Ounis, Professor Iadh
• Authors: MacDonald, C., and Ounis, I.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science; Q Science > QA Mathematics > QA76 Computer software
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: ACM Press
• ISBN: 1595934332
• Copyright Holders: Copyright © 2006 ACM Press
• First Published: First published in Proceedings of the 15th ACM International Conference on Information and Knowledge Management
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher.