社交编码平台中的协作专家发现

Roohollah Etemadi, Morteza Zihayat, Kuan Feng, Jason Adelman, E. Bagheri
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引用次数: 1

摘要

像GitHub这样的在线社交编码(SC)平台由于其社交功能和在产品开发生命周期中的巨大支持而越来越受欢迎。专家对存储库贡献的丰富信息可以用来为新的/现有的项目招募专家。在本文中,我们定义了SC平台中的协同专家寻找问题。给定一个项目,我们将SC平台建模为一个属性异构网络,以端到端方式学习网络实体的潜在表示,并利用它们来发现协作专家以完成项目。在GitHub的真实世界数据集上进行的大量实验表明,就一系列性能指标而言,所提出的方法优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative Experts Discovery in Social Coding Platforms
The popularity of online social coding (SC) platforms such as GitHub is growing due to their social functionalities and tremendous support during the product development lifecycle. The rich information of experts' contributions on repositories can be leveraged to recruit experts for new/existing projects. In this paper, we define the problem of collaborative experts finding in SC platforms. Given a project, we model an SC platform as an attributed heterogeneous network, learn latent representations of network entities in an end-to-end manner and utilize them to discover collaborative experts to complete a project. Extensive experiments on real-world datasets from GitHub indicate the superiority of the proposed approach over the state-of-the-art in terms of a range of performance measures.
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