TY - GEN
T1 - Talent Demand-Supply Joint Prediction with Dynamic Heterogeneous Graph Enhanced Meta-Learning
AU - Guo, Zhuoning
AU - Liu, Hao
AU - Zhang, Le
AU - Zhang, Qi
AU - Zhu, Hengshu
AU - Xiong, Hui
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (Grant No. 62102110, 61836013) and Foshan HKUST Projects (FSUST21-FYTRI01A, FSUST21-FYTRI02A). And this work was done when the first author interned in Baidu Research.
Publisher Copyright:
© 2022 ACM.
PY - 2022/8/14
Y1 - 2022/8/14
N2 - 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.
AB - 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.
KW - demand-supply modeling
KW - heterogeneous graph neural network
KW - labor market forecasting
KW - meta-learning
KW - sequential modeling
UR - http://www.scopus.com/inward/record.url?scp=85137147595&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137147595&partnerID=8YFLogxK
U2 - 10.1145/3534678.3539139
DO - 10.1145/3534678.3539139
M3 - Conference contribution
AN - SCOPUS:85137147595
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2957
EP - 2967
BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Y2 - 14 August 2022 through 18 August 2022
ER -