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Modeling document features for expert finding

Zhu, Jianhan; Song, Dawei; Rüger, Stefan and Huang, Xiangji (2008). Modeling document features for expert finding. In: Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08, p. 1421.

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We argue that expert finding is sensitive to multiple document features in an organization, and therefore, can benefit from the incorporation of these document features. We propose a unified language model, which integrates multiple document features, namely, multiple levels of associations, PageRank, indegree, internal document structure, and URL length. Our experiments on two TREC Enterprise Track collections, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios.

Item Type: Conference Item
Copyright Holders: 2008 The Authors
Keywords: expert finding; language models; enterprise search
Academic Unit/Department: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Item ID: 25887
Depositing User: Kay Dave
Date Deposited: 04 Jan 2011 12:21
Last Modified: 05 Oct 2016 08:55
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