EURO 2025 Leeds
Abstract Submission

102. Analyzing and Visualizing Job Skill Requirements on Job Search Websites

Invited abstract in session MD-34: Advancements of OR-analytics in statistics, machine learning and data science 1, stream Advancements of OR-analytics in statistics, machine learning and data science.

Monday, 14:30-16:00
Room: Michael Sadler LG10

Authors (first author is the speaker)

1. Li-Ching Ma
Department of Information Management, National United University
2. Cheng-Syuan Lin

Abstract

The prevalence of the Internet has led to an explosion of data from various sources, often resulting in information overload. Therefore, finding ways to help users efficiently organize and understand vast amounts of information has become an important issue. Visual representations can simplify and aggregate complex information into meaningful patterns, helping people understand their decision-making environment and make better decisions.
In human resource management, the job market also presents a large amount of recruitment information every day. When graduating college students are preparing to look for a job, they usually need to spend a lot of time browsing numerous job postings on recruitment websites to understand the abilities and skill requirements for a specific job category. This study proposes a graphical approach for analyzing and visualizing online recruitment information on a two-dimensional plane using mathematical programming optimization and grouping methods. Taking a well-known online job search website in Taiwan as an example, the proposed process and its corresponding results are illustrated.
Users can directly see the capabilities, skill requirements, and their relationships for a specific job category on a simple graph. This can help them be better prepared before applying for a job.

Keywords

Status: accepted

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