CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship

2024-08-28

arXiv.org

AI Summary

Existing career trajectory prediction methods overlook the dynamic evolution of user–job–company ternary relationships, failing to capture feature drift and path dependency inherent in career progression. To address this, we propose the first temporal knowledge graph (TKG)-based framework for ternary dynamic modeling, jointly learning time-aware dependencies among users, jobs, and companies. Our method comprises four components: TKG construction, ternary relational embedding, dynamic graph neural networks, and an extrapolative reasoning mechanism designed to capture fine-grained labor market mobility patterns. Evaluated on real-world career datasets, our approach achieves 6.80% and 34.58% absolute improvements in company and job prediction accuracy, respectively—outperforming four baseline models as well as state-of-the-art CTP and TKG methods. The framework establishes a novel, interpretable, and extrapolation-capable paradigm for career trajectory prediction.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Planning, Routing, and Scheduling: Temporal Planning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions--i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively. The codebase of CAPER is available at https://github.com/Bigdasgit/CAPER.

Problem

• Career Trajectory Prediction
• Complex Triadic Relationships
• Temporal Dynamics

Innovation

• CAPER
• Temporal Knowledge Graphs
• Career Trajectory Prediction