Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks

TL;DR

This paper introduces PJFCANN, a neural network model that improves person-job fit estimation by incorporating historical recruitment data using co-attention and graph neural networks, outperforming existing methods.

Contribution

The paper presents a novel neural network approach that integrates recruitment history into person-job fit estimation, enhancing matching accuracy over traditional content-based methods.

Findings

PJFCANN outperforms state-of-the-art baselines on large-scale datasets.

Incorporating recruitment history improves matching accuracy.

Co-attention and graph neural networks effectively capture semantic and historical information.

Abstract

Existing online recruitment platforms depend on automatic ways of conducting the person-job fit, whose goal is matching appropriate job seekers with job positions. Intuitively, the previous successful recruitment records contain important information, which should be helpful for the current person-job fit. Existing studies on person-job fit, however, mainly focus on calculating the similarity between the candidate resumes and the job postings on the basis of their contents, without taking the recruiters' experience (i.e., historical successful recruitment records) into consideration. In this paper, we propose a novel neural network approach for person-job fit, which estimates person-job fit from candidate profile and related recruitment history with co-attention neural networks (named PJFCANN). Specifically, given a target resume-job post pair, PJFCANN generates local semantic representations through co-attention neural networks and global experience representations via graph neural networks. The final matching degree is calculated by combining these two representations. In this way, the historical successful recruitment records are introduced to enrich the features of resumes and job postings and strengthen the current matching process. Extensive experiments conducted on a large-scale recruitment dataset verify the effectiveness of PJFCANN compared with several state-of-the-art baselines. The codes are released at: https://github.com/CCIIPLab/PJFCANN.