Earn Hub: A Student-Centric Local Gig Marketplace

The proposed Earn Hub: A Student-Centric Local Gig Marketplace provides a simple and effective platform for connecting students with local, part-time, and short-term job opportunities. The system allows students to create profiles by providing their skills, interests, and location and helps them find suitable job opportunities through a centralized platform. The proposed system makes the job search process easier by providing important job details such as required skills, location, payment, and duration. Students can apply for suitable jobs and track their application status through the platform. Earn Hub helps students utilize their skills, gain practical experience, and find flexible earning opportunities. Overall, the proposed system provides a user-friendly solution for student-focused employment and local gig opportunities. It reduces the effort involved in searching for suitable jobs and provides an organized platform for students and job providers to connect.

[1]  A. Seveso, A. Giabelli, L. Malandri, F. Mercorio, and M. Mezzanzanica, “Skills2Job: A Recommender System that Encodes Job Offer Embeddings on Graph Databases,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 18, pp. 15885–15886, 2021. [2]  “Learning-Based Matched Representation System for Job Recommendation,” Computers, vol. 11, no. 11, 161, 2022. The work focuses on matching job seekers' skills with job requirements using NLP and machine-learning techniques. [3]  B. L. Foo, C. Y. Ting, H. N. Goh, A. Quek, and C. L. Cham, “Dynamic Job Recommendation by Profiling Undergraduates Academic Performances,” Journal of Informatics and Web Engineering, 2025. [4]  “Design and implementation of student job matching system based on personalized recommendation algorithm,” Systems and Soft Computing, vol. 7, 200302, 2025. [5]  “Machine learning for labor market matching,” Machine Learning with Applications, vol. 23, 100861, 2026. [6]  D. Spoladore, S. Criscuolo, and F. Isgrò, “Representing jobs and job seekers in AI-based recommender systems: A literature review,” Engineering Applications of Artificial Intelligence, vol. 174, 114450, 2026