JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning

TL;DR

This paper introduces JAMES, a novel multi-embedding and reasoning-based approach for normalizing diverse and non-standard job titles in online marketplaces, significantly improving accuracy over existing methods.

Contribution

JAMES combines graph, contextual, and syntactic embeddings with a multi-aspect co-attention and logical reasoning to enhance job title normalization.

Findings

JAMES outperforms baseline models by over 10% in Precision@10.

JAMES achieves a 17.52% improvement in NDCG@10.

The approach effectively handles large-scale, long-tailed job title data.

Abstract

In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e.g., job recommendation, users' career analysis, and turnover prediction). Job Title Normalization (JTN) is such a cleaning step to classify user-created non-standard job titles into normalized ones. However, solving the JTN problem is non-trivial with challenges: (1) semantic similarity of different job titles, (2) non-normalized user-created job titles, and (3) large-scale and long-tailed job titles in real-world applications. To this end, we propose a novel solution, named JAMES, that constructs three unique embeddings (i.e., graph, contextual, and syntactic) of a target job title to effectively capture its various traits. We further propose a multi-aspect co-attention mechanism to attentively combine these embeddings, and employ neural logical reasoning representations to collaboratively estimate similarities between messy job titles and normalized job titles in a reasoning space. To evaluate JAMES, we conduct comprehensive experiments against ten competing models on a large-scale real-world dataset with over 350,000 job titles. Our experimental results show that JAMES significantly outperforms the best baseline by 10.06% in Precision@10 and by 17.52% in NDCG@10, respectively.