Abstract
Person-Job Fit (PJF) is the core of online recruitment. Several recent methods took PJF as a preference-learning problem, and tried to learn two-sided preferences from their historical behaviors. However, they ignored users’ interactive feedbacks (accepted or rejected) received from the other side which may change users’ preferences. In addition, they neglected the status of local market which may affect the final matching results of person-job pairs.
To solve these issues, we propose a market-aware dynamic PJF method with hierarchical reinforcement learning (HIRE). We design a two-level hierarchy of reinforcement learning policies. Two low-level policies aim to learn the dynamic preferences of both persons and jobs with consideration of interactive feedbacks, and a high-level policy aims to learn the optimal dynamic matching strategy with consideration of local market state. Extensive experiments on two real-world datasets show the effectiveness of HIRE compared with the state-of-the-art.
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Acknowledgements
This work was partially sponsored by National 863 Program of China (Grant No. 2015AA016009).
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Fu, B. et al. (2022). Market-Aware Dynamic Person-Job Fit with Hierarchical Reinforcement Learning. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13246. Springer, Cham. https://doi.org/10.1007/978-3-031-00126-0_54
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DOI: https://doi.org/10.1007/978-3-031-00126-0_54
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