enhancing expertise retrieval using community-aware strategies
Expertise retrieval has received increased interests in recent years, whose task is to suggest people with relevant expertise. Motivated by the observation that communities could provide valuable insight and distinctive information, we investigate two community-aware strategies to enhance expertise retrieval. We first propose a new smoothing method using the community context instead of the whole collection for statistical language model in the document-based model. Furthermore, a query-sensitive AuthorRank is proposed to model the authors' authorities according to the community co-authorship networks, and then an adaptive ranking refinement method is developed to further enhance expertise retrieval. Experimental results demonstrate the effectiveness and robustness of both community-aware strategies.
expertise retrieval community language model authorrank