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Zhu, Jianhan; Song, Dawei; Rüger, Stefan and Huang, Xiangji
(2008).
DOI: https://doi.org/10.1145/1458082.1458312
Abstract
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.
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About
- Item ORO ID
- 25887
- Item Type
- Conference or Workshop Item
- Keywords
- expert finding; language models; enterprise search
- Academic Unit or School
-
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 - Copyright Holders
- © 2008 The Authors
- Depositing User
- Kay Dave