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題名 X2-Search: Contextual expert search in social networks
作者 Li, C.-T.;Shan, Man-Kwan
沈錳坤
貢獻者 資科系
關鍵詞 Artificial intelligence; Social networking (online); Bibliography datum; Contextual knowledge; Effectiveness and efficiencies; Expert searches; Online social networkings; Practical problems; Social connection; Team formation; Search engines
日期 2013-12
上傳時間 26-五月-2015 18:28:28 (UTC+8)
摘要 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. © 2013 IEEE.
關聯 Proceedings - 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013, 2013, 論文編號 6783863, 176-181, 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013; Taipei; Taiwan; 6 December 2013 到 8 December 2013; 類別編號E2528; 代碼 104746
資料類型 conference
DOI http://dx.doi.org/10.1109/TAAI.2013.44
dc.contributor 資科系
dc.creator (作者) Li, C.-T.;Shan, Man-Kwan
dc.creator (作者) 沈錳坤zh_TW
dc.date (日期) 2013-12
dc.date.accessioned 26-五月-2015 18:28:28 (UTC+8)-
dc.date.available 26-五月-2015 18:28:28 (UTC+8)-
dc.date.issued (上傳時間) 26-五月-2015 18:28:28 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75331-
dc.description.abstract (摘要) 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. © 2013 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceedings - 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013, 2013, 論文編號 6783863, 176-181, 2013 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2013; Taipei; Taiwan; 6 December 2013 到 8 December 2013; 類別編號E2528; 代碼 104746
dc.subject (關鍵詞) Artificial intelligence; Social networking (online); Bibliography datum; Contextual knowledge; Effectiveness and efficiencies; Expert searches; Online social networkings; Practical problems; Social connection; Team formation; Search engines
dc.title (題名) X2-Search: Contextual expert search in social networks
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/TAAI.2013.44
dc.doi.uri (DOI) http://dx.doi.org/10.1109/TAAI.2013.44