JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning
This addresses the need for a well-defined job title taxonomy in online job marketplaces to improve tasks like job recommendation and career analysis, representing an incremental advance in domain-specific normalization techniques.
The paper tackled the problem of Job Title Normalization (JTN) by proposing JAMES, a method that uses multi-aspect embeddings and reasoning to classify non-standard job titles into normalized ones, achieving improvements of 10.06% in Precision@10 and 17.52% in NDCG@10 over the best baseline.
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.