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Professional Network Matters: Connections Empower Person-Job Fit (2401.00010v1)

Published 19 Dec 2023 in cs.SI and cs.LG

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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Authors (9)
  1. Hao Chen (1006 papers)
  2. Lun Du (50 papers)
  3. Yuxuan Lu (26 papers)
  4. Qiang Fu (159 papers)
  5. Xu Chen (413 papers)
  6. Shi Han (74 papers)
  7. Yanbin Kang (3 papers)
  8. Guangming Lu (49 papers)
  9. Zi Li (33 papers)

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