论文标题
JPLINK：将工作链接到职业兴趣类型
JPLink: On Linking Jobs to Vocational Interest Types
论文作者
论文摘要
将求职者与相关的工作联系起来，不仅需要根据技能，而且性格类型进行匹配。尽管荷兰代码也被称为RIASEC经常被用来通过对六种不同类别的职业的适用性分组，但在工作职位中通常找不到单个工作的RIASEC类别标签。这归因于用RIASEC标签分配工作职位所需的重大手动工作。为了应付使用RIASEC标签分配大量作业，我们建议使用JPLINK，这是一种使用作业标题和作业描述中的文本内容的机器学习方法。 JPLINK利用域知识在特定于职业的知识库中可用，称为O*NET，以提高职位的功能表示。为了纳入每个作业的RIASEC标签的相对排名，Jplink提出了一个受学习排名启发的列表损失函数。我们的定量和定性评估都表明，Jplink的表现要优于常规基准。我们对JPLINK的预测进行了错误分析，以表明它可以发现现有工作职位中的标签错误。
Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits domain knowledge available in an occupation-specific knowledge base known as O*NET to improve feature representation of job posts. To incorporate relative ranking of RIASEC labels of each job, JPLink proposes a listwise loss function inspired by learning to rank. Both our quantitative and qualitative evaluations show that JPLink outperforms conventional baselines. We conduct an error analysis on JPLink's predictions to show that it can uncover label errors in existing job posts.
