Quasi-Metric Learning for Bilateral Person-Job Fit

Matching suitable jobs with qualified candidates is crucial for online recruitment. Typically, users (i.e., candidates and employers) have specific expectations in the recruitment market, making them prefer similar jobs or candidates. Metric learning technologies provide a promising way to capture the similarity propagation between candidates and jobs. However, they rely on symmetric distance measures, failing to model users' asymmetric relationships in two-way selection. Additionally, users' behaviors (e.g., candidates) are highly affected by the feedback from their counterparts (e.g., employers), which can hardly be captured by the existing person-job fit methods that primarily explore homogeneous and undirected graphs. To address these problems, we propose a quasi-metric learning framework to capture the similarity propagation between candidates and jobs while modeling their asymmetric relations for bilateral person-job fit. Specifically, we propose a quasi-metric space that not only satisfies the triangle inequality to capture the fine-grained similarity between candidates and jobs, but also incorporates a tailored asymmetric measure to model users. two-way selection process in online recruitment. More importantly, the proposed quasi-metric learning framework can theoretically model recruitment rules from similarity and competitiveness perspectives, making it seamlessly align with bilateral person-job fit scenarios. To explore the mutual effects of two-sided users, we first organize candidates, employers, and their different-typed interactions into a heterogeneous relation graph, and then propose a relation-aware graph convolution network to capture users. mutual effects through their bilateral behaviors. Extensive experiments on several real-world datasets demonstrate the effectiveness of the proposed methods.