KARRIEREWEGE: A Large Scale Career Path Prediction Dataset
This provides a valuable resource for stakeholders like job seekers and recruiters, though it is incremental as it builds on existing datasets and methods.
The authors tackled the scarcity of public data for career path prediction by introducing KARRIEREWEGE, a dataset with over 500k career paths linked to the ESCO taxonomy, and KARRIEREWEGE+ with synthesized text to handle free-text inputs, resulting in improved performance and robustness for SOTA models, particularly in free-text use cases.
Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce KARRIEREWEGE, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in KARRIEREWEGE+. This allows for accurate predictions from unstructured data, closely aligning with real-world application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a prior benchmark and observe improved performance and robustness, particularly for free-text use cases, due to the synthesized data.