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Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning

Published: 14 August 2022 Publication History

Abstract

Talent demand and supply forecasting aims to model the variation of the labor market, which is crucial to companies for recruitment strategy adjustment and to job seekers for proactive career path planning. However, existing approaches either focus on talent demand or supply forecasting, but overlook the interconnection between demand-supply sequences among different companies and positions. To this end, in this paper, we propose a Dynamic Heterogeneous Graph Enhanced Meta-learning (DH-GEM) framework for fine-grained talent demand-supply joint prediction. Specifically, we first propose a Demand-Supply Joint Encoder-Decoder (DSJED) and a Dynamic Company-Position Heterogeneous Graph Convolutional Network (DyCP-HGCN) to respectively capture the intrinsic correlation between demand and supply sequences and company-position pairs. Moreover, a Loss-Driven Sampling based Meta-learner (LDSM) is proposed to optimize long-tail forecasting tasks with a few training data. Extensive experiments have been conducted on three real-world datasets to demonstrate the effectiveness of our approach compared with five baselines. DH-GEM has been deployed as a core component of the intelligent human resource system of a cooperative partner.

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    cover image ACM Conferences
    KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
    August 2022
    5033 pages
    ISBN:9781450393850
    DOI:10.1145/3534678
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    Published: 14 August 2022

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    Author Tags

    1. demand-supply modeling
    2. heterogeneous graph neural network
    3. labor market forecasting
    4. meta-learning
    5. sequential modeling

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    • (2024)ARFGCN: Adaptive Receptive Field Graph Convolutional Network for Urban Crowd Flow PredictionMathematics10.3390/math1211173912:11(1739)Online publication date: 3-Jun-2024
    • (2024)Pre-DyGAEProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/222(2009-2017)Online publication date: 3-Aug-2024
    • (2024)A cross-view hierarchical graph learning hypernetwork for skill demand-supply joint predictionProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i18.29956(19813-19822)Online publication date: 20-Feb-2024
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