Summary of Caper: Enhancing Career Trajectory Prediction Using Temporal Knowledge Graph and Ternary Relationship, by Yeon-chang Lee et al.
CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship
by Yeon-Chang Lee, JaeHyun Lee, Michiharu Yamashita, Dongwon Lee, Sang-Wook Kim
First submitted to arxiv on: 28 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the problem of career trajectory prediction (CTP), aiming to forecast one’s future employer or job position. The authors argue that existing CTP methods overlook the complex interdependencies between key units (user, position, and company) in a career, leading to inaccurate job movement pattern understanding. To resolve this, they propose CAPER, a novel solution that leverages temporal knowledge graph (TKG) modeling to capture shifts in key units over time. This enables more accurate predictions of future companies and positions. The authors conduct experiments on a real-world CTP dataset and demonstrate that CAPER outperforms various state-of-the-art methods by significant margins. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAPER is a new way to predict where people will work in the future. Right now, we can’t accurately forecast job movements because we’re not considering all the important things that affect someone’s career, like their job, company, and personal background. CAPER uses a special type of computer model called a temporal knowledge graph (TKG) to understand how these things change over time. This helps CAPER make more accurate predictions about where people will work in the future. |
Keywords
» Artificial intelligence » Knowledge graph
