Modeling and exploiting heterogeneous bibliographic networks for expertise ranking
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
A joint classification method to integrate scientific and social networks
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Expertise retrieval in bibliographic network: a topic dominance learning approach
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Expert group formation using facility location analysis
Information Processing and Management: an International Journal
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Expertise retrieval, whose task is to suggest people with relevant expertise on the topic of interest, has received increasing interest in recent years. One of the issues is that previous algorithms mainly consider the documents associated with the experts while ignoring the community information that is affiliated with the documents and the experts. Motivated by the observation that communities could provide valuable insight and distinctive information, we investigate and develop two community-aware strategies to enhance expertise retrieval. We first propose a new smoothing method using the community context for statistical language modeling, which is employed to identify the most relevant documents so as to boost the performance of expertise retrieval in the document-based model. Furthermore, we propose a query-sensitive AuthorRank to model the authors' authorities based on the community coauthorship networks and develop an adaptive ranking refinement method to enhance expertise retrieval. Experimental results demonstrate the effectiveness and robustness of both community-aware strategies. Moreover, the improvements made in the enhanced models are significant and consistent.