Your Profile Reveals Your Traits in Talent Market: An Enhanced Person-Job Fit Representation Learning

Abstract Person-job fit lies at the heart of online recruitment, measuring the compatibility between job seekers and vacancies. Current researchers mainly focus on job-resume matching between job requirements and work experiences, presenting two notable limitations. Free text representation is constrained by word-level polysemy and sentence-level anisotropy. Also, overreliance on final representations hampers the exploration of inter-feature relationships. Towards this end, we proposed a novel Attentive Person-Job Fit Multifaceted feature Fusion model (APJFMF), aiming at obtaining more precise and comprehensive interactive person-job fit feature representations. The main contributions are: (1) Introduction of a method for multifaceted feature extraction and fusion from multi-source heterogeneous person-job data; (2) Enhanced the person-job free text representations through unsupervised fine-tuning of BERT; (3) Investigated global feature interactions by integrating diverse attention mechanisms. Extensive experiments on a real-world recruitment dataset confirmed the effectiveness of APJFMF and its individual components. The code is released at https://github.com/raochongzhi/APJFMF.