Ejxpert finding for collaborative virtual environments
Ejxpert finding for collaborative virtual environments
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DOI：
10.1145/501317.501343
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发表时间：
2001-12-01
影响因子：
22.7
通讯作者：
House, D
中科院分区：
文献类型：
--
作者：
Maybury, M;D'Amore, R;House, D
56 December 2001/Vol. 44, No. 12 COMMUNICATIONS OF THE ACM priate data was available. By “precision,” we measure the degree to which a staff member found by Expert Finder is considered expert by humans. By “recall,” we mean the degree to which a priori human-designated experts are found by the Expert Finder (that is, the number of actual experts found divided by the total number of experts reported, and the number of actual experts found divided by total number of experts, respectively).In contrast to the query-based Expert Finder tool, XperNet [2] focuses on finding expert communities of practice using clustering and network analysis techniques. Networks of individuals with related skills and interests are created by processing information about staff project information, publications, and personal Web pages. Expertise indicators (for example, explicit reference or citation, network centrality) as well as
