research-article

Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph

Authors:

Enhong ChenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 4

Article No.: 110, Pages 1 - 28

https://doi.org/10.1145/3651158

Published: 26 April 2024 Publication History

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.

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Cited By

Sun QZha RZhang LZhou JMei YLi ZXiong HBaeza-Yates RBonchi F(2024)CrossLight: Offline-to-Online Reinforcement Learning for Cross-City Traffic Signal ControlProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671927(2765-2774)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671927

Index Terms

Towards Unified Representation Learning for Career Mobility Analysis with Trajectory Hypergraph
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 4

July 2024

751 pages

EISSN:1558-2868

DOI:10.1145/3613639

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 26 April 2024

Online AM: 06 March 2024

Accepted: 18 February 2024

Revised: 13 January 2024

Received: 02 June 2023

Published in TOIS Volume 42, Issue 4

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Cited By

Sun QZha RZhang LZhou JMei YLi ZXiong HBaeza-Yates RBonchi F(2024)CrossLight: Offline-to-Online Reinforcement Learning for Cross-City Traffic Signal ControlProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671927(2765-2774)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671927

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