大型工业和专业职业数据集
A Large-scale Industrial and Professional Occupation Dataset
摘要：
人们对利用职业数据采掘和分析越来越感兴趣。在当今的就业市场中，职业数据采掘和分析的重要性越来越大，因为它使公司能够预测员工流动率，建立职业轨迹模型，筛选简历并执行其他人力资源任务。促进这些任务的一个关键要求是需要一个与职业有关的数据集。然而，大多数研究使用专有数据集或不公开数据集，从而阻碍了这一领域的发展。为了解决这个问题，我们提出了工业和专业职业数据集（IPOD），其中包括属于56 k LinkedIn用户的192 k职位。除了公开IPOD，我们还：（i）手动注释每个职位及其相关的资历级别，工作领域和位置;以及（ii）提供职位嵌入并讨论各种用例。该数据集可在https://github.com/junhua/ipod上公开获得。
英文摘要：
There has been growing interest in utilizing occupational data mining and analysis. In today's job market, occupational data mining and analysis is growing in importance as it enables companies to predict employee turnover, model career trajectories, screen through resumes and perform other human resource tasks. A key requirement to facilitate these tasks is the need for an occupation-related dataset. However, most research use proprietary datasets or do not make their dataset publicly available, thus impeding development in this area. To solve this issue, we present the Industrial and Professional Occupation Dataset (IPOD), which comprises 192k job titles belonging to 56k LinkedIn users. In addition to making IPOD publicly available, we also: (i) manually annotate each job title with its associated level of seniority, domain of work and location; and (ii) provide embedding for job titles and discuss various use cases. This dataset is publicly available at https://github.com/junhua/ipod.
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。
