Job2Vec：基于集体多视图表示学习的职位基准
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
摘要：
职位基准（JTB）旨在将不同公司的职位与相似的专业知识水平相匹配。JTB可以为人才招聘和求职者提供准确的指导和相当大的便利，以进行职位和薪酬校准/预测。传统的JTB方法主要依赖于人工市场调查，这是昂贵和劳动密集型的。近年来，在线职业图谱的快速发展积累了大量的人才职业记录，这为数据驱动的解决方案提供了一个很好的趋势。然而，这仍然是一项具有挑战性的任务，因为（1）职位名称和职位转换（跳槽）数据是混乱的，其中包含许多针对同一职位的主观和非标准命名约定（例如，程序员、软件开发工程师、工程师、实施工程师），（2）存在大量缺失的职位/过渡信息，以及（3）一个人才只寻求有限数量的工作，这带来了建模工作过渡模式的不完整性和随机性。为了克服这些挑战，我们聚合所有的记录来构建一个大规模的职位基准图（Job-Graph），其中节点表示与特定公司相关的职位，链接表示职位之间的相关性。我们将JTB重新定义为在Job-Graph上进行链接预测的任务，匹配的职位应该有链接。沿着这条线，我们提出了一个集体的多视图表示学习方法（Job 2 Vec），通过检查工作图联合在（1）图拓扑视图，（2）语义视图，（3）工作过渡平衡视图，（4）工作过渡持续时间视图。我们融合的多视图表示的编码-解码范例，以获得一个统一的最佳表示的任务的链接预测。最后，我们进行了大量的实验，以验证我们提出的方法的有效性。
英文摘要：
Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor-intensive. Recently, the rapid development of Online Professional Graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e.g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view, (2)semantic view, (3) job transition balance view, and (4) job transition duration view. We fuse the multi-view representations in the encode-decode paradigm to obtain a unified optimal representation for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
