Modeling Two-Way Selection Preference for Person-Job Fit

TL;DR

This paper introduces a dual-perspective graph learning method to model two-way preferences in person-job matching, capturing both successful and failed interactions for improved recruitment accuracy.

Contribution

It proposes a novel dual-perspective graph representation learning approach that models bidirectional preferences and interactions in person-job fit, addressing limitations of unidirectional models.

Findings

Outperforms existing methods on three real-world datasets.

Effectively models both successful and failed matchings.

Enhances understanding of two-way recruitment preferences.

Abstract

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach.