tagcombine：recommending tags to contents in software information sites.pdfVIP

Wang XY, Xia X, Lo D. TagCombine: Recommending tags to contents in software information sites. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 30(5): 1017–1035 Sept. 2015. DOI 10.1007/s11390-015-1578-2 TagCombine: Recommending Tags to Contents in Software Information Sites Xin-Yu Wang 1 (#‰), Xin Xia 1,∗ (g ), Member, CCF, ACM, IEEE, and David Lo 2, Member, ACM, IEEE 1College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2School of Information Systems, Singapore Management University, Singapore, Singapore E-mail: {wangxinyu, xxia}@; davidlo@.sg Received March 20, 2015; revised July 9, 2015. Abstract Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three di昇erent components: 1) multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2) similarity-based ranking component which recommends tags from similar objects; 3) tag-term based ranking component which considers the relationship between di昇erent terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Di昇erent, Ask Ubuntu, Freecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recall@10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and