Abstract
In an Enterprise setting, an expert search system can assist users with their “expertise need” by suggesting people with relevant expertise to the topic of interest. These systems typically work by associating documentary evidence of expertise to each candidate expert, and then ranking the candidates by the extent to which the documents in their profile are about the query. There are three important factors that affect the retrieval performance of an expert search system - firstly, the selection of the candidate profiles (the documents associated with each candidate), secondly, how the topicality of the documents is measured, and thirdly how the evidence of expertise from the associated documents is combined. In this work, we investigate a new dimension to expert finding, namely whether some documents are better indicators of expertise than others in each candidate’s profile. We apply five techniques to predict the quality documents in candidate profiles, which are likely to be good indicators of expertise. The techniques applied include the identification of possible candidate homepages, and of clustering the documents in each profile to determine the candidate’s main areas of expertise. The proposed approaches are evaluated on three expert search task from recent TREC Enterprise tracks and provide conclusions.
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Macdonald, C., Hannah, D., Ounis, I. (2008). High Quality Expertise Evidence for Expert Search. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds) Advances in Information Retrieval. ECIR 2008. Lecture Notes in Computer Science, vol 4956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78646-7_27
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DOI: https://doi.org/10.1007/978-3-540-78646-7_27
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