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FedPJF: federated contrastive learning for privacy-preserving person-job fit

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Abstract

The person-job fit algorithm has become a crucial task in the online recruitment industry for matching resumes with suitable jobs and making recommendations. However, individuals’ resumes and users’ interaction records contain personal privacy information, which resumes and records cannot use in a public environment. This paper proposes a person-job fit framework with federated learning and privacy protection. It can utilize the user data stored locally on the user-side to train the model and ensure that the user’s private data are not uploaded to the server-side. In addition, it prevents users’ uploaded gradients from revealing private information by applying adaptive differential privacy techniques. However, federated learning suffers from a nonindependent homogeneous distribution of user data, which can lead to a user-side learned job representation with its own bias. Therefore, we introduce the idea of contrastive learning to guide user-side training to alleviate user-side bias and improve model performance. Experiments on real datasets show that our framework ensures higher performance while preserving user privacy data compared to typical person-job fit methods.

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Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This research is supported by the Natural Science Foundation of Zhejiang (LZ20F020001), the Natural Science Foundation of Ningbo (2021J091), and the Science and Technology Innovation 2025 Major Project of Ningbo (No.20211ZDYF020036).

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, 315211, Ningbo, Zhejiang, China

    Yunchong Zhang, Baisong Liu & Jiangbo Qian

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  1. Yunchong Zhang
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Correspondence to Baisong Liu.

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Zhang, Y., Liu, B. & Qian, J. FedPJF: federated contrastive learning for privacy-preserving person-job fit. Appl Intell 53, 27060–27071 (2023). https://doi.org/10.1007/s10489-023-04775-2

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