Professional Network Matters: Connections Empower Person-Job Fit

• URL: http://arxiv.org/abs/2401.00010v1
• Date: Tue, 19 Dec 2023 06:44:44 GMT
• Title: Professional Network Matters: Connections Empower Person-Job Fit
• Authors: Hao Chen, Lun Du, Yuxuan Lu, Qiang Fu, Xu Chen, Shi Han, Yanbin Kang, Guangming Lu, Zi Li
• Abstract summary: This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn.
• Score: 62.20651880558674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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