JAMES: Job Title Mapping with Multi-Aspect Embeddings and Reasoning

Abstract

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. Recently [20] built a 30,000 job title taxonomy on LinkedIn for a job understanding task. However, little is known about how the JTM task with the aforementioned challenges can be solved.Proposed Ideas. Toward these challenges, in this paper, we propose JAMES (Job title mApping with Multi-aspect Embeddings and rea Soning) to solve the JTM task. We use a large-scale and real-world career dataset with more than 350,000 job titles that a