An Intelligent Freelancing Platform with Content - Based Filtering and Secure Payment Integration

Abstract

This study presents the design and implementation of an intelligent freelancing platform aimed at bridging the gap between skilled professionals and clients seeking digital services. The platform addresses prevalent challenges such as ineffective job matching, payment security, and user experience limitations. Utilizing a content-based filtering algorithm, it delivers personalized job recommendations by analysing freelancer profiles and job requirements. To ensure secure financial transactions, the platform integrates trusted payment gateways alongside escrow mechanisms. The system is developed using a modern technology stack, featuring React.js for the frontend, Node.js and Express.js for backend services, and a combination of PostgreSQL and MongoDB for data storage. This architecture supports scalability, responsiveness, and robust data handling. The development process follows a structured methodology encompassing planning, system design, feature implementation, integration, and deployment phases. Initial assessments demonstrate enhanced accuracy in matching freelancers with suitable projects, improved collaboration between clients and freelancers, and increased transaction security. These improvements contribute to creating a trustworthy and accessible digital marketplace that supports the evolving gig economy.