Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph

Author:

Zha Rui1ORCID,Sun Ying2ORCID,Qin Chuan3ORCID,Zhang Le4ORCID,Xu Tong1ORCID,Zhu Hengshu5ORCID,Chen Enhong6ORCID

Affiliation:

1. University of Science and Technology of China, Hefei, China

2. Thrust of Artificial Intelligence, HKUST (Guangzhou), Guangzhou, China

3. Career Science Lab, BOSS Zhipin, Beijing, China and PBC School of Finance, Tsinghua University, Beijing, China

4. Baidu Research, Baidu Inc., Beijing China

5. Career Science Lab, BOSS Zhipin, Beijing, China and Thrust of Artificial Intelligence, HKUST (Guangzhou), Guangzhou, China

6. University of Science and Technology of China, Hefei, China

Abstract

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.

Funder

National Natural Science Foundation of China

USTC Research Funds of the Double First-Class Initiative

Publisher

Association for Computing Machinery (ACM)

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