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| 基于多维特征的开源项目个性化推荐方法 |
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杨程1,2, 范强1,2, 王涛1,2, 尹刚1,2, 王怀民1,2
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1.并行与分布处理国家重点实验室(国防科学技术大学), 湖南 长沙 410073;2.国防科学技术大学 计算机学院, 湖南 长沙 410073
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| 摘要: |
| 随着软件协同开发技术与社交网络的深度融合，社交化开发范式已成为当前软件创作与生产的重要方式.这一软件开发模型的灵活性与开放性，吸引了大规模的外围贡献者加入到开源社区中，形成了巨大的软件生产力.在开源社区中，这些分布广泛、规模巨大的外围贡献者，主要以一种无组织的松散方式进行协同.他们需要花费大量的时间和精力，在海量的开源项目中寻找到自己真正感兴趣的项目并进行长期贡献.为了提高大规模群体协同的效率，提出一种基于多维特征的开源项目个性化推荐方法（即RepoLike）.该方法从开源项目自身流行度、关联项目技术相关度以及大众贡献者之间的社交关联度这3个维度度量开发者和开源项目之间的关联关系，并利用线性组合和Learning To Rank方法构建推荐模型，从而为开发者提供个性化的项目推荐服务.通过大规模的实验，其结果表明： RepoLike在推荐20个候选项目时的推荐命中率超过25%，能够有效地为开发人员提供有价值的推荐服务. |
| 关键词: 社交化编程 开源项目 个性化推荐 Learning to Rank GitHub |
| DOI：10.13328/j.cnki.jos.005230 |
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| 基金项目:国家自然科学基金（61432020，61472430，61502512）；国家重点研发计划（2016YFB1000805） |
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| Multi-Feature Based Personal Recommendation Approach for Open Source Project |
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YANG Cheng1,2, FAN Qiang1,2, WANG Tao1,2, YIN Gang1,2, WANG Huai-Min1,2
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1.Key Laboratory of Parallel and Distributed Computing(National University of Defense Technology), Changsha 410073, China;2.School of Computer, National University of Defense Technology, Changsha 410073, China
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| Abstract: |
| With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Due to the flexibility and openness, a large number of external contributors are attracted to the open source communities. They are playing a significant role in open source development. However, the online open source development is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks to work on from thousands of open source projects in the communities. In this paper, a new approach, called RepoLike, is proposed for recommending repositories to developers based on linear combination and learning to rank. It utilizes the project popularity, technical dependencies among projects and social connections among developers to measure the correlations between a developer and the given projects. The experiment results show that this new approach can achieve over 25% of hit ratio when recommending 20 candidates, which means it can recommend closely correlated repositories to social developers. |
| Key words: social coding open source software personal recommendation Learning to Rank GitHub |
