X2-Search: Contextual expert search in social networks

Cheng Te Li, Man Kwan Shan

Research output: Contribution to conferencePaperpeer-review

4 Citations (Scopus)


Searching experts in online social networking services, such as Linked In, is an important and practical problem which has been studied recently. While existing works rely on simply social structure or only personal skills to locate experts, the contextual knowledge, derived from combining skills with social connections, is missed. For example, one may wish to find experts who master at financial and are well-connected with engineers in the Bay Area. By leveraging the social contexts as the search clues, this work proposes and develops a Contextual Expert Search (X2-Search) system to discover desired experts and teams. X2-Search provides two major functions, Specialist Finding and Team Formation. Given a set of target and context labels of skills, our system aims to return a ranked list of individuals or teams satisfying the query requirement. Experiments conducted on DBLP bibliography data show the promising effectiveness and efficiency of X2-Search. In the application practice, X2-Search system is built on Linked In, and can be extended to the social and expertise data in other domains.

Original languageEnglish
Number of pages6
Publication statusPublished - 2013 Jan 1
Event2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013 - Taipei, Taiwan
Duration: 2013 Dec 62013 Dec 8


Other2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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