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CIKM
2006
Springer

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

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Voting for candidates: adapting data fusion techniques for an expert search task
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 particu...
Craig Macdonald, Iadh Ounis
Added 13 Oct 2010
Updated 13 Oct 2010
Type Conference
Year 2006
Where CIKM
Authors Craig Macdonald, Iadh Ounis
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