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Voting techniques for expert search

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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, called the Voting Model for Expert Search. In the Voting Model, we see the problem of ranking experts as a voting problem. We model the voting problem using 12 various voting techniques, which are inspired from the data fusion field. We investigate the effectiveness of the Voting Model and the associated voting techniques across a range of document weighting models, in the context of the TREC 2005 and TREC 2006 Enterprise tracks. The evaluation results show that the voting paradigm is very effective, without using any query or collection-specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the voting 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 voting techniques for the proposed approach is stable on a given task regardless of the used weighting models, suggesting that some of the proposed voting techniques will always perform better than other voting techniques.

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Authors and Affiliations

  1. Department of Computing Science, University of Glasgow, Glasgow, G12 8QQ, Scotland, UK

    Craig Macdonald & Iadh Ounis

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  1. Craig Macdonald
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  2. Iadh Ounis
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Correspondence to Craig Macdonald.

Additional information

Extended version of ‘Voting for candidates: adapting data fusion techniques for an expert search task’. C. Macdonald and I. Ounis. In Proceedings of ACM CIKM 2006, Arlington, VA. 2006. doi: 10.1145/1183614.1183671.

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Macdonald, C., Ounis, I. Voting techniques for expert search. Knowl Inf Syst 16, 259–280 (2008). https://doi.org/10.1007/s10115-007-0105-3

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