A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Tapping on the potential of q&a community by recommending answer providers
Proceedings of the 17th ACM conference on Information and knowledge management
A probability model for related entity retrieval using relation pattern
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Finding expert users in community question answering
Proceedings of the 21st international conference companion on World Wide Web
A Relation Pattern-Driven Probability Model for Related Entity Retrieval
International Journal of Knowledge and Systems Science
Finding topic-level experts in scholarly networks
Scientometrics
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Searching an organization's document repositories for experts is a frequently faced problem in intranet information management. This paper proposes a candidate-centered model which is referred as Candidate Description Document (CDD)-based retrieval model. The expertise evidence about an expert candidate scattered over repositories is mined and aggregated automatically to form a profile called the candidate's CDD, which represents his knowledge. We present the model from its foundations through its logical development and argue in favor of this model for expert finding. We devise and compare the different strategies for exploring a variety of expertise evidence. The experiments on TREC enterprise corpora demonstrate that the CDD-based model achieves significant and consistent improvement on performance through comparative studies with non-CDD methods.