Professional Network Matters: Connections Empower Person-Job Fit
Hao Chen Lun Du Yuxuan Lu Qiang Fu Xu Chen Shi Han Yanbin Kang Guangming Lu Zi Li
Online recruitment platforms typically employ Person-Job Fit models in the
core service that automatically match suitable job seekers with appropriate job
positions. While existing works leverage historical or contextual information,
they often disregard a crucial aspect: job seekers' social relationships in
professional networks. This paper emphasizes the importance of incorporating
professional networks into the Person-Job Fit model. Our innovative approach
consists of two stages: (1) defining a Workplace Heterogeneous Information
Network (WHIN) to capture heterogeneous knowledge, including professional
connections and pre-training representations of various entities using a
heterogeneous graph neural network; (2) designing a Contextual Social Attention
Graph Neural Network (CSAGNN) that supplements users' missing information with
professional connections' contextual information. We introduce a job-specific
attention mechanism in CSAGNN to handle noisy professional networks, leveraging
pre-trained entity representations from WHIN. We demonstrate the effectiveness
of our approach through experimental evaluations conducted across three
real-world recruitment datasets from LinkedIn, showing superior performance
compared to baseline models.