The impact of person-job fit on job satisfaction and performance is widely acknowledged, which highlights the importance of providing workers with next steps at the right time in their career. This task of predicting the next step in a career is known as career path prediction, and has diverse applications such as turnover prevention and internal job mobility. Existing methods to career path prediction rely on large amounts of private career history data to model the interactions between job titles and companies. We propose leveraging the unexplored textual descriptions that are part of work experience sections in resumes. We introduce a structured dataset of 2,164 anonymized career histories, annotated with ESCO occupation labels. Based on this dataset, we present a novel representation learning approach, CareerBERT, specifically designed for work history data. We develop a skill-based model and a text-based model for career path prediction, which achieve 35.24% and 39.61% recall@10 respectively on our dataset. Finally, we show that both approaches are complementary as a hybrid approach achieves the strongest result with 43.01% recall@10.

翻译：人岗匹配对工作满意度与绩效的影响已被广泛认可，这凸显了在职业生涯适当时机为员工提供发展建议的重要性。预测职业下一步发展轨迹的任务被称为职业路径预测，其在离职预防、内部岗位流动等领域具有广泛应用。现有职业路径预测方法依赖大量私密职业历史数据来建模职位与公司间的交互关系。我们提出利用简历工作经历模块中尚未被开发的文本描述信息，构建了包含2,164份匿名职业历史的结构化数据集，并标注了ESCO职业分类标签。基于该数据集，我们提出了专为工作历史数据设计的表征学习方法CareerBERT，开发了基于技能与基于文本的职业路径预测模型，在测试集上分别取得35.24%和39.61%的recall@10。最后证明两种方法具有互补性，混合模型达到43.01%的recall@10最优性能。