IEA: an answerer recommendation approach on stack overflow
IEA: an answerer recommendation approach on stack overflow作者机构:State Key Laboratory of Software Development Environment Beihang University
出 版 物:《Science China(Information Sciences)》 (中国科学:信息科学(英文版))
年 卷 期:2019年第62卷第11期
页 面:51-69页
核心收录:
学科分类:081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:supported by National Key Research and Development Program of China(Grant No.2018YFB1004202) National Natural Science Foundation of China(Grant No.61672078) State Key Laboratory of Software Development Environment of China(Grant No.SKLSDE-2018ZX-12)
主 题:answerer recommendation activeness comments topical interest topical expertise stack overflow
摘 要: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 than30% 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 containing41950 questions, 64894 answers, and 96960 comments. In comparison with the state-of-the-art approaches of TEM, TTEA and TTEA-ACT, IEA improves n DCG 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%.