Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph

Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in...

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Veröffentlicht in:ACM transactions on information systems 2024-04, Vol.42 (4), p.1-28, Article 110
Hauptverfasser: Zha, Rui, Sun, Ying, Qin, Chuan, Zhang, Le, Xu, Tong, Zhu, Hengshu, Chen, Enhong
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Sprache:eng
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Zusammenfassung:Career mobility analysis aims at understanding the occupational movement patterns of talents across distinct labor market entities, which enables a wide range of talent-centered applications, such as job recommendation, labor demand forecasting, and company competitive analysis. Existing studies in this field mainly focus on a single fixed scale, investigating either individual trajectories at the micro-level or crowd flows among market entities at the macro-level. Consequently, the intrinsic cross-scale interactions between talents and the labor market are largely overlooked. To bridge this gap, we propose UniTRep, a novel unified representation learning framework for cross-scale career mobility analysis. Specifically, we first introduce a trajectory hypergraph structure to organize the career mobility patterns in a low-information-loss manner, where market entities and talent trajectories are represented as nodes and hyperedges, respectively. Then, for learning the market-aware talent representations, we attentively propagate the node information to the hyperedges and incorporate the market contextual features into the process of individual trajectory modeling. For learning the trajectory-enhanced market representations, we aggregate the message from hyperedges associated with a specific node to integrate the fine-grained semantics of trajectories into labor market modeling. Moreover, we design two auxiliary tasks to optimize both intra-scale and cross-scale learning with a self-supervised strategy. Extensive experiments on a real-world dataset clearly validate that UniTRep can significantly outperform state-of-the-art baselines for various tasks.
ISSN:1046-8188
1558-2868
DOI:10.1145/3651158