Retrieving people: Identifying potential answerers in Community Question‐Answering

Long T. Le and Chirag Shah

Journal of the Association for Information Science & Technology, 2018, vol. 69, issue 10, 1246-1258

Abstract: Community Question‐Answering (CQA) sites have become popular venues where people can ask questions, seek information, or share knowledge with a user community. Although responses on CQA sites are obviously slower than information retrieved by a search engine, one of the most frustrating aspects of CQAs occurs when an asker's posted question does not receive a reasonable answer or remains unanswered. CQA sites could improve users' experience by identifying potential answerers and routing appropriate questions to them. In this paper, we predict the potential answerers based on question content and user profiles. Our approach builds user profiles based on past activity. When a new question is posted, the proposed method computes scores between the question and all user profiles to find the potential answerers. We conduct extensive experimental evaluations on two popular CQA sites ‐ Yahoo! Answers and Stack Overflow ‐ to show the effectiveness of our algorithm. The results show that our technique is able to predict a small group of 1000 users from which at least one user will answer the question with a probability higher than 50% in both CQA sites. Further analysis indicates that topic interest and activity level can improve the correctness of our approach.

Date: 2018
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