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

TL;DR

CAPER leverages temporal knowledge graphs and ternary relationships to improve career trajectory prediction by capturing dynamic job movement patterns and mutual dependencies among user, position, and company.

Contribution

This paper introduces CAPER, a novel TKG-based method that jointly models ternary dependencies and temporal shifts for more accurate career trajectory prediction.

Findings

CAPER outperforms four baseline methods in predicting future companies and positions.

CAPER achieves 6.80% and 34.58% higher accuracy compared to baselines.

The approach effectively captures dynamic career movement patterns over time.

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.