  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.
