TagCombine： Recommending Tags to Contents in Software Information Sites

作     者：王新宇 夏鑫 David Lo Xin-Yu Wang;Xin Xia;David Lo

作者机构：1.College of Computer Science and Technology Zhejiang University Hangzhou 310027 China 2.School of Information Systems Singapore Management University Singapore Singapore 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2015年第30卷第5期

页      面：1017-1035页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0835[工学-软件工程] 081201[工学-计算机系统结构] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by China Knowledge Centre for Engineering Sciences and Technology the National Key Technology Research and Development Program of the Ministry of Science and Technology of China the Fundamental Research Funds for the Central Universities of China 

主　　题：software information site online media tag recommendation 

摘      要：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 different 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 different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Feecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recallS10 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 the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.