Enhanced enterprise-student matching with meta-path based graph neural network

IF 5.2 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Fu Li , Guangsheng Ma , Feier Chen , Qiuyun Lyu , Zhen Wang , Jian Zhang
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引用次数: 0

Abstract

Job-seeking is always an inescapable challenge for graduates. It may take a lot of time to find satisfying jobs due to the information gap between students who need satisfying offers and enterprises which ask for proper candidates. Although campus recruiting and job advertisements on the Internet could provide partial information, it is still not enough to help students and enterprises know each other and effectively match a graduate with a job. To narrow the information gap, we propose to recommend jobs for graduates based on historical employment data. Specifically, we construct a heterogeneous information network to characterize the relations between students, enterprises and industries. And then, we propose a meta-path based graph neural network, namely GraphRecruit, to further learn both latent student and enterprise portrait representations. The designed meta-paths connect students with their preferred enterprises and industries from different aspects. Also, we apply genetic algorithm optimization for meta-path selection according to application scenarios to enhance recommendation suitability and accuracy. To show the effectiveness of GraphRecruit, we collect five-year employment data and conduct extensive experiments comparing GraphRecruit with 4 classical baselines. The results demonstrate the superior performance of the proposed method.

利用基于元路径的图神经网络增强企业与学生的匹配度
求职始终是毕业生无法回避的挑战。学生需要满意的工作,企业需要合适的人选,由于信息不对等,学生可能需要花费大量时间才能找到满意的工作。虽然校园招聘和互联网上的招聘广告可以提供部分信息,但仍不足以帮助学生和企业相互了解并有效地为毕业生匹配工作。为了缩小信息差距,我们建议根据历史就业数据为毕业生推荐工作。具体来说,我们构建了一个异构信息网络来描述学生、企业和行业之间的关系。然后,我们提出了一种基于元路径的图神经网络,即 GraphRecruit,以进一步学习潜在的学生和企业肖像表征。所设计的元路径从不同方面将学生与他们喜欢的企业和行业联系起来。此外,我们还根据应用场景应用遗传算法优化元路径选择,以提高推荐的适宜性和准确性。为了证明 GraphRecruit 的有效性,我们收集了五年的就业数据,并进行了大量实验,将 GraphRecruit 与 4 个经典基线进行比较。实验结果证明了所提出方法的卓越性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.50
自引率
8.70%
发文量
656
审稿时长
29 days
期刊介绍: In 2022 the Journal of King Saud University - Computer and Information Sciences will become an author paid open access journal. Authors who submit their manuscript after October 31st 2021 will be asked to pay an Article Processing Charge (APC) after acceptance of their paper to make their work immediately, permanently, and freely accessible to all. The Journal of King Saud University Computer and Information Sciences is a refereed, international journal that covers all aspects of both foundations of computer and its practical applications.
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