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Professional Network Matters: Connections Empower Person-Job Fit

Published: 04 March 2024 Publication History

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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cover image ACM Conferences
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining
March 2024
1246 pages
ISBN:9798400703713
DOI:10.1145/3616855
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Published: 04 March 2024

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Author Tags

  1. graph neural network
  2. heterogeneous information network
  3. person-job fit

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