Software information sites such as StackOverflow and Freeecode enable information sharing and communication for developers around the world. To facilitate correct classification and efficient search, developers need to provide tags for their postings. However, tagging is inherently an uncoordinated process that depends not only on developers’ understanding of their own postings but also on other factors, including developers’ English skills and knowledge about existing postings. As a result, developers keep creating new tags even though existing tags are sufficient. The net effect is an ever increasing number of tags with severe redundancy along with more postings over time. Any algorithms based on tags become less efficient and accurate. In this paper we propose FastTagRec, an automated scalable tag recommendation method using neural network-based classification. By learning existing postings and their tags from existing information, FastTagRec is able to very accurately infer tags for new postings. We have implemented a prototype tool and carried out experiments on ten software information sites. Our results show that FastTagRec is not only more accurate but also three orders of magnitude faster than the comparable state-of-the-art tool TagMulRec. In addition to empirical evaluation, we have also conducted an user study which successfully confirms the usefulness of of our approach.

中文翻译：

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FastTagRec：软件信息站点的快速标签推荐

StackOverflow 和 Freeecode 等软件信息站点为世界各地的开发人员提供信息共享和交流。为了便于正确分类和高效搜索，开发者需要为他们的帖子提供标签。然而，标记本质上是一个不协调的过程，不仅取决于开发人员对自己帖子的理解，还取决于其他因素，包括开发人员的英语技能和对现有帖子的了解。因此，即使现有标签就足够了，开发人员仍会不断创建新标签。最终结果是，随着时间的推移，具有严重冗余的标签数量不断增加，同时发布的内容也越来越多。任何基于标签的算法都变得不那么高效和准确。在本文中，我们提出了 FastTagRec，一种使用基于神经网络的分类的自动可扩展标签推荐方法。通过从现有信息中学习现有帖子及其标签，FastTagRec 能够非常准确地推断新帖子的标签。我们已经实现了一个原型工具，并在十个软件信息站点上进行了实验。我们的结果表明，FastTagRec 不仅更准确，而且比同类最先进的工具 TagMulRec 快三个数量级。除了实证评估外，我们还进行了一项用户研究，成功证实了我们方法的实用性。我们已经实现了一个原型工具，并在十个软件信息站点上进行了实验。我们的结果表明，FastTagRec 不仅更准确，而且比同类最先进的工具 TagMulRec 快三个数量级。除了实证评估外，我们还进行了一项用户研究，成功证实了我们方法的实用性。我们已经实现了一个原型工具，并在十个软件信息站点上进行了实验。我们的结果表明，FastTagRec 不仅更准确，而且比同类最先进的工具 TagMulRec 快三个数量级。除了实证评估外，我们还进行了一项用户研究，成功证实了我们方法的实用性。