A Recommendation System Based on a Microservice Architecture to Avoid Workplace Stress

Stress in the workplace is a major problem that affects people of all ages, backgrounds, and occupations. It can contribute to various health problems, from anxiety to insomnia, among others. Workplace stress significantly impacts employee well-being and productivity. Current stress-management approaches, while valuable, primarily address stress after it has occurred. This highlights the critical need for proactive systems capable of anticipating individual stress and preventing negative health consequences. This research presents the design and initial implementation of a novel microservice-based recommendation system for proactively mitigating workplace stress among computer users. The system leverages predicted stress levels to deliver timely, personalized, and easily implemented interventions. This study focuses on evaluating the system’s architecture, core functionalities, and initial performance using a content-based filtering approach. A pilot study demonstrated the system’s feasibility, highlighting areas for future development.