Stack overflow is a web-based service where users can seek information by asking questions and share knowledge by providing answers about software development. Ideally, new questions are assigned to experts and answered within a short time after their submissions. However, the number of new questions is very large on stack overflow, answerers are not easy to find suitable questions timely. Therefore, an answerer recommendation approach is required to assign appropriate questions to answerers. In this paper, we make an empirical study about developers’ activities. Empirical results show that 66.24% of users have more than 30% of comment activities. Furthermore, active users in the previous day are likely to be active in the next day. In this paper, we propose an approach IEA which combines user topical interest, topical expertise and activeness to recommend answerers for new questions. We first model user topical interest and expertise based on historical questions and answers. We also build a calculation method of users’ activeness based on historical questions, answers, and comments. We evaluate the performance of IEA on 3428 users containing 41950 questions, 64894 answers, and 96960 comments. In comparison with the state-of-the-art approaches of TEM, TTEA and TTEA-ACT, IEA improves nDCG by 2.48%, 3.45% and 3.79%, and improves Pearson rank correlation coefficient by 236.20%, 84.91% and 224.12%, and improves Kendall rank correlation coefficient by 424.18%, 1845.30% and 772.60%.
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This work was supported by National Key Research and Development Program of China (Grant No. 2018YFB1004202), National Natural Science Foundation of China (Grant No. 61672078), and State Key Laboratory of Software Development Environment of China (Grant No. SKLSDE-2018ZX-12).
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Wang, L., Zhang, L. & Jiang, J. IEA: an answerer recommendation approach on stack overflow. Sci. China Inf. Sci. 62, 212103 (2019). https://doi.org/10.1007/s11432-018-9848-2
- answerer recommendation
- topical interest
- topical expertise
- stack overflow