ExpertRank: A topic-aware expert finding algorithm for online knowledge communities

  • Authors:

  • G. Alan Wang;Jian Jiao;Alan S. Abrahams;Weiguo Fan;Zhongju Zhang

  • Affiliations:

  • Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States;Department of Computer Science, Virginia Tech, 114 McBryde Hall, Blacksburg, VA 24061, United States;Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States;Department of Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 3007 Pamplin Hall, Blacksburg, VA 24061, United States and School of Information, Zhejiang University ...;Operations and Information Management Department, School of Business, University of Connecticut, 2100 Hillside Road, Unit 1041, Storrs, CT 06269, United States

  • Venue:
  • Decision Support Systems
  • Year:
  • 2013

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Abstract

With increasing knowledge demands and limited availability of expertise and resources within organizations, professionals often rely on external sources when seeking knowledge. Online knowledge communities are Internet based virtual communities that specialize in knowledge seeking and sharing. They provide a virtual media environment where individuals with common interests seek and share knowledge across time and space. A large online community may have millions of participants who have accrued a large knowledge repository with millions of text documents. However, due to the low information quality of user-generated content, it is very challenging to develop an effective knowledge management system for facilitating knowledge seeking and sharing in online communities. Knowledge management literature suggests that effective knowledge management should make accessible not only written knowledge but also experts who are a source of information and can perform a given organizational or social function. Existing expert finding systems evaluate one's expertise based on either the contents of authored documents or one's social status within his or her knowledge community. However, very few studies consider both indicators collectively. In addition, very few studies focus on virtual communities where information quality is often poorer than that in organizational knowledge repositories. In this study we propose a novel expert finding algorithm, ExpertRank, that evaluates expertise based on both document-based relevance and one's authority in his or her knowledge community. We modify the PageRank algorithm to evaluate one's authority so that it reduces the effect of certain biasing communication behavior in online communities. We explore three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. We evaluate ExpertRank using a popular online knowledge community. Experiments show that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered.