OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets

Despite substantial advancements in various fields of AI, computational research in career and job domains has been significantly hindered by a critical lack of accessible datasets. This limitation is mainly due to the proprietary nature of job platforms, which restrict the sharing of job-domain datasets with the research community. The scarcity is particularly pronounced for career trajectory and resume datasets, severely constraining academic researchers in developing and evaluating new models. In this paper, we address the crucial issue of resume dataset unavailability in the job domain, identified through our comprehensive comparison of existing job-domain machine learning studies. To the best of our knowledge, we introduce OpenResume, the first publicly available, anonymized, and structured resume dataset, specifically designed for job-domain downstream tasks. This dataset aims to catalyze advancements in AI and foster new markets for machine learning and data science within career trajectory modeling. OpenResume is comprehensively processed from real-world resume data. We anonymize and substitute personal identifiers and company names, normalize job titles into ESCO-based ones (i.e., one of the most common occupation taxonomies), and employ differential privacy techniques on temporal features to ensure open accessibility and privacy protection. Additionally, we augment OpenResume with a synthetically generated resume dataset derived from the post-processed real-world data, extending its diversity and utility. To demonstrate that OpenResume retains challenges and properties similar to real-world job datasets, we benchmark OpenResume on state-of-the-art job-domain prediction models across four prevalent downstream tasks: (1) next job title prediction, (2) next company prediction, (3) turnover prediction, and (4) link prediction. Our experimental results show that these job-domain models perform comparably on OpenResume and the original data across all tasks, demonstrating OpenResume as a valuable career trajectory dataset for both academic research and practical applications. We also indicate the OpenResume applicability for the other eight downstream tasks. Our datasets are available at: https://tinyurl.com/OpenResumeData.

Authors

Institutions

Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825519
Citations
2
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.69
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets

Michiharu Yamashita, Dongwon Lee, Thanh Tran
2 citations
Machine Learning in Healthcare
0.69
article

OpenResume: Advancing Career Trajectory Modeling with Anonymized and Synthetic Resume Datasets

Michiharu Yamashita, Dongwon Lee, Thanh Tran
article en
2 citations

Abstract

Despite substantial advancements in various fields of AI, computational research in career and job domains has been significantly hindered by a critical lack of accessible datasets. This limitation is mainly due to the proprietary nature of job platforms, which restrict the sharing of job-domain datasets with the research community. The scarcity is particularly pronounced for career trajectory and resume datasets, severely constraining academic researchers in developing and evaluating new models. In this paper, we address the crucial issue of resume dataset unavailability in the job domain, identified through our comprehensive comparison of existing job-domain machine learning studies. To the best of our knowledge, we introduce OpenResume, the first publicly available, anonymized, and structured resume dataset, specifically designed for job-domain downstream tasks. This dataset aims to catalyze advancements in AI and foster new markets for machine learning and data science within career trajectory modeling. OpenResume is comprehensively processed from real-world resume data. We anonymize and substitute personal identifiers and company names, normalize job titles into ESCO-based ones (i.e., one of the most common occupation taxonomies), and employ differential privacy techniques on temporal features to ensure open accessibility and privacy protection. Additionally, we augment OpenResume with a synthetically generated resume dataset derived from the post-processed real-world data, extending its diversity and utility. To demonstrate that OpenResume retains challenges and properties similar to real-world job datasets, we benchmark OpenResume on state-of-the-art job-domain prediction models across four prevalent downstream tasks: (1) next job title prediction, (2) next company prediction, (3) turnover prediction, and (4) link prediction. Our experimental results show that these job-domain models perform comparably on OpenResume and the original data across all tasks, demonstrating OpenResume as a valuable career trajectory dataset for both academic research and practical applications. We also indicate the OpenResume applicability for the other eight downstream tasks. Our datasets are available at: https://tinyurl.com/OpenResumeData.

Pennsylvania State University (US), Amazon (United States) (US)
Gender equality
Openalex Percentile: Top 22%
Machine Learning in Healthcare
0.69
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.