›› 2015,Vol. 30 ›› Issue (5): 1017-1035.doi: 10.1007/s11390-015-1578-2

所属专题： Data Management and Data Mining； Software Systems

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇

TagCombine:一种为软件信息网站推荐标签的方法

Xin-Yu Wang1(王新宇), Xin Xia1,*(夏鑫), Member, CCF, ACM, IEEE, David Lo2, Member, ACM, IEEE   

  1. 1 College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
    2 School of Information Systems, Singapore Management University, Singapore, Singapore
  • 收稿日期:2015-03-20 修回日期:2015-07-09 出版日期:2015-09-05 发布日期:2015-09-05
  • 通讯作者: Xin Xia E-mail:xxia@zju.edu.cn
  • 作者简介:Xin-Yu Wang received his Bachelor's and Ph.D. degrees in computer science from Zhejiang University, Hangzhou, in 2002 and 2007 respectively. He was a research assistant in Zhejiang University during 2002~2007. He is currently an associate professor in the College of Computer Science and Technology, Zhejiang University. His research interests include software engineering, formal methods, and very large information systems.
  • 基金资助:

    This research was partially supported by China Knowledge Centre for Engineering Sciences and Technology under Grant No. CKCEST-2014-1-5, the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant Nos. 2015BAH17F01 and 2013BAH01B01, and the Fundamental Research Funds for the Central Universities of China.

TagCombine: Recommending Tags to Contents in Software Information Sites

Xin-Yu Wang1(王新宇), Xin Xia1*(夏鑫), Member, CCF, ACM, IEEE, David Lo2, Member, ACM, IEEE   

  1. 1 College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;
    2 School of Information Systems, Singapore Management University, Singapore, Singapore
  • Received:2015-03-20 Revised:2015-07-09 Online:2015-09-05 Published:2015-09-05
  • Contact: Xin Xia E-mail:xxia@zju.edu.cn
  • About author:Xin-Yu Wang received his Bachelor's and Ph.D. degrees in computer science from Zhejiang University, Hangzhou, in 2002 and 2007 respectively. He was a research assistant in Zhejiang University during 2002~2007. He is currently an associate professor in the College of Computer Science and Technology, Zhejiang University. His research interests include software engineering, formal methods, and very large information systems.
  • Supported by:

    This research was partially supported by China Knowledge Centre for Engineering Sciences and Technology under Grant No. CKCEST-2014-1-5, the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant Nos. 2015BAH17F01 and 2013BAH01B01, and the Fundamental Research Funds for the Central Universities of China.

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如今, 软件工程师热衷于在大量网络媒体中搜寻探索新鲜有趣的技术, 并与其他工程师相互学习和帮助。我们将这类能够帮助软件工程师提高软件开发维护和测试性能的网络媒体称为软件信息网站。在本文中, 我们提出了一种软件信息网站中自动标签推荐方法, 称为TagCombine。TagCombine由三个不同的组件组成, 分别是:1.多标记排序组件, 将标签推荐考虑成一个多标记学习问题;2.相似度排序组件, 通过搜寻相似的对象来推荐标签;3.基于标签-词的排序组件, 通过分析对象中不同单词和标签之间的关系来推荐标签。我们在四个软件信息网站上进行实验来评估TagCombine的性能, 它们是AskDifferent, AskUbuntu, Freecode和StackOverflow。平均来说, TagCombine的recall@5和recall@10 的值是0.6198 和0.7625, 我们的方法比Al-Kofahi等人论文提出的TagRec方法提高了14.56%和10.55%, 比Zangerle等人论文提出的标签推荐方法提高了12.08%和8.16%。

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 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, 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 the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.

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