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.

中文翻译：

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Job2Vec：使用集体多视图表示学习的职位名称基准测试

职称基准 (JTB) 旨在匹配不同公司中具有相似专业知识水平的职称。JTB可以为人才招聘和求职者的职位和薪酬校准/预测提供精确的指导和相当大的便利。传统的 JTB 方法主要依赖于人工市场调查，成本高且劳动密集。近期，Online Professional Graph 的快速发展积累了大量的人才职业记录，为数据驱动的解决方案提供了良好的发展趋势。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）数据很混乱，其中包含许多对同一职位（例如程序员、软件开发工程师）的主观和非标准命名约定, SDE, 实施工程师), (2) 大量缺失的职称/过渡信息，以及 (3) 一个人才只寻找有限数量的工作，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，我们汇总了所有记录以构建大规模的职位名称基准图（Job-Graph），其中节点表示隶属于特定公司的职位，链接表示职位之间的相关性。我们将 JTB 重新定义为对匹配职位应该有链接的 Job-Graph 进行链接预测的任务。沿着这条线，我们通过在 (1) 图拓扑视图、(2) 语义视图、(3) 工作转换平衡视图和 (4) 中联合检查作业图，提出了一种集体多视图表示学习方法 (Job2Vec)作业转换持续时间视图。我们融合了编码-解码范式中的多视图表示，以获得链接预测任务的统一最优表示。最后，我们进行了广泛的实验来验证我们提出的方法的有效性。