One of the most essential tasks needed for various downstream tasks in career
analytics (e.g., career trajectory analysis, job mobility prediction, and job
recommendation) is Job Title Mapping (JTM), where the goal is to map
user-created (noisy and non-standard) job titles to predefined and standard job
titles. However, solving JTM is domain-specific and non-trivial due to its
inherent challenges: (1) user-created job titles are messy, (2) different job
titles often overlap their job requirements, (3) job transition trajectories
are inconsistent, and (4) the number of job titles in real world applications
is large-scale. Toward this JTM problem, in this work, we propose a novel
solution, named as JAMES, that constructs three unique embeddings of a target
job title: topological, semantic, and syntactic embeddings, together with
multi-aspect co-attention. In addition, we employ logical reasoning
representations to collaboratively estimate similarities between messy job
titles and standard job titles in the reasoning space. We conduct comprehensive
experiments against ten competing models on the large-scale real-world dataset
with more than 350,000 job titles. Our results show that JAMES significantly
outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in
NDCG@10, respectively.

本文提出一种名为 JAMES 的解决方案，通过三种不同类型的嵌入以及多方面协同注意力和逻辑推理表征技术，可将无序、非标准的职位信息映射到预定义的标准职位上。在真实数据上的实验表明，JAMES 策略在准确度和 NDCG 方面可以分别比最佳对比基线高出 10.06％和 17.52％。