Journal article
Harnessing global expertise: A comparative study of expertise profiling methods for online communities
Information systems frontiers, Vol.16(4), pp.715-727
09/2014
DOI: 10.1007/s10796-012-9385-6
Abstract
Building expertise profiles in global online communities is a critical step in leveraging the range of expertise available in the global knowledge economy. In this paper we introduce a three-stage framework that automatically generates expertise profiles of online community members. In the first two stages, document-topic relevance and user-document association are estimated for calculating users’ expertise levels on individual topics. We empirically compare two state-of-the-art information retrieval techniques, the vector space model and the language model, with a Latent Dirichlet Allocation (LDA) based model for computing document-topic relevance as well as the direct and indirect association models for computing user-document association. In the third stage we test whether a filtering strategy can improve the performance of expert profiling. Our experimental results using two real datasets provide useful insights on how to select the best models for profiling users’ expertise in online communities that can work across a range of global communities.
Details
- Title: Subtitle
- Harnessing global expertise: A comparative study of expertise profiling methods for online communities
- Creators
- Xiaomo Liu - Department of Computer Science Virginia Tech Blacksburg VA 24061 USAG. Alan Wang - Department of Business Information Technology Virginia Tech Blacksburg VA 24061 USAAditya Johri - Department of Engineering Education Virginia Tech Blacksburg VA 24061 USAMi Zhou - School of Management Xi’an Jiaotong University Xi’an Shanxi 710049 People’s Republic of ChinaWeiguo Fan - Department of Accounting and Information Systems Virginia Tech Blacksburg VA 24061 USA
- Resource Type
- Journal article
- Publication Details
- Information systems frontiers, Vol.16(4), pp.715-727
- Publisher
- Springer US
- DOI
- 10.1007/s10796-012-9385-6
- ISSN
- 1387-3326
- eISSN
- 1572-9419
- Language
- English
- Date published
- 09/2014
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083220102771
Metrics
7 Record Views