Development of a Recommendation System for Resume Tracking and Job Suggestions

This paper introduces a recommendation system to improve resume tracking and provide personalized job suggestions, focusing on contributing to sustainable hiring practices. In today's job market, many candidates struggle to find roles that match their skills, leading to inefficient job searches. Our system uses Generative artificial intelligence (AI), natural language processing (NLP), and web scraping to analyze resumes and match them with relevant job descriptions. By automating these processes, the system not only increases the accuracy of job matching but also reduces the manual effort required from job seekers and recruiters. This contributes to sustainability by saving time, lowering resource consumption, and reducing the environmental impact of traditional hiring methods. The system also supports economic sustainability by helping individuals find suitable employment faster, promoting inclusive growth, and reducing unemployment. The results indicate that this approach improves job matching accuracy, making recruitment processes more efficient and helping candidates enhance their visibility in automated hiring systems. Overall, this system fosters a more sustainable and effective job market aligned with global sustainability goals.