Abstract
Community Question Answering (CQA) sites facilitate users to ask questions and get answered by fellow users interested in the topic of the question. A vast number of questions are posted on these sites every day. Some questions receive numbers of good quality answers whereas some questions fail to attract even a single answer from the community users. Also, some questions receive very late answers. The problem behind the unanswered question or late answers was that the question was not seen or not routed to the expert user or interested users. There are no identified experts of given topic on these sites. Hence, finding users who will be interested in answering a question of the specific topic and sending the question to that user is a challenging task. We have developed a system to identify the group of users who can potentially be the answerer of a given question. The group of users is identified using their past question and answers. We rank the users of the identified group considering their answering behaviour, time of posting their answers etc. The proposed methodology has several advantages such as routing questions to recently active users and at the time of their convenience. Experimental analysis shows that to get at least one answer, the question must be routed to at least eight answerers.
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Roy, P.K., Singh, J.P., Nag, A. (2018). Finding Active Expert Users for Question Routing in Community Question Answering Sites. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_33
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