Open Source Repository Recommendation in Social Coding
Open Source Repository Recommendation in Social Coding
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DOI：
10.1145/3077136.3080753
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Jyun-Yu Jiang;Pu-Jen Cheng;Wei Wang
中科院分区：
文献类型：
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作者：
Jyun-Yu Jiang;Pu-Jen Cheng;Wei Wang
社交编码和开源存储库变得越来越流行。软件开发人员有各种各样的选择来为社区做出贡献并与他人合作。然而，目前在学术界和工业界都没有有效的推荐程序来推荐开发人员合适的存储库。虽然现有的一类协同过滤（OCCF）的方法可以应用于这个问题，他们不考虑特定的约束，如编程语言，在某种程度上，关联的存储库与开发人员的社会编码。本文的目的是研究利用用户编程语言偏好来提高基于OCCF的知识库推荐性能的可行性。在矩阵分解的基础上，我们提出了语言正则化矩阵分解（LRMF），它是通过用户编程语言偏好之间的关系来正则化的。在GitHub的真实世界数据集上进行了广泛的实验。结果表明，我们的框架显着优于五个有竞争力的基线。
Social coding and open source repositories have become more and more popular. Software developers have various alternatives to contribute themselves to the communities and collaborate with others. However, nowadays there is no effective recommender suggesting developers appropriate repositories in both the academia and the industry. Although existing one-class collaborative filtering (OCCF) approaches can be applied to this problem, they do not consider particular constraints of social coding such as the programming languages, which, to some extent, associate the repositories with the developers. The aim of this paper is to investigate the feasibility of leveraging user programming language preference to improve the performance of OCCF-based repository recommendation. Based on matrix factorization, we propose language-regularized matrix factorization (LRMF), which is regularized by the relationships between user programming language preferences. Extensive experiments have been conducted on the real-world dataset of GitHub. The results demonstrate that our framework significantly outperforms five competitive baselines.