Early prediction of promising expert users on community question answering sites

被引:0
作者
Roy, Pradeep Kumar [1 ]
Singh, Jyoti Prakash [2 ]
机构
[1] Indian Inst Informat Technol, Dept Comp Sci & Engn, Surat, Gujarat, India
[2] Natl Inst Technol Patna, Dept Comp Sci & Engn, Patna, India
关键词
Community question answering; Expert users; Question routing; Classification; Machine learning; ATMOSPHERIC DISPERSION MODEL; ARTIFICIAL NEURAL-NETWORK; AL MATRIX NANOCOMPOSITES; DATA ASSIMILATION; GENETIC ALGORITHM; RISK-ASSESSMENT; OPTIMIZATION; REMOVAL; SYSTEM;
D O I
10.1007/s13198-024-02303-0
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Community question answering (CQA) sites have become a popular medium for exchanging knowledge with other members of the community. Users can publish questions, answers, and comments on these sites. Furthermore, users of the CQA sites are able to express their thoughts on a post by voting positively or negatively. People anticipate rapid and high-quality answers from these CQA sites, which are often provided by a small group of users known as experts. A large number of queries remain unanswered on these forums, emphasising the scarcity of experts. To address this problem, we presented a methodology for predicting promising expert users for CQA sites. Promising experts are individuals that have just joined the community and have shown glimpses of producing high-quality content to the site. The suggested method looks at the first month of a user's postings to determine whether or not the individual is a promising expert. The experimental findings revealed that the suggested approach accurately predicts future experts.
引用
收藏
页码:2902 / 2913
页数:12
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