JobSwipe

Abstract

The rapid digitization of recruitment platforms has significantly transformed how job seekers and employers interact. Despite the widespread adoption of platforms such as LinkedIn, Indeed, and Monster, the recruitment process remains inefficient due to information overload, poor filtering precision, and lack of mutual engagement mechanisms. These platforms rely heavily on text-based browsing models, resulting in cognitive fatigue and reduced engagement. This project introduces JobSwipe, a cross-platform mobile application that revolutionizes recruitment through a swipe-based matching mechanism inspired by modern social applications. The system ensures that communication between job seekers and recruiters occurs only after mutual interest is established, thereby improving relevance and reducing unnecessary interactions. The application is developed using Flutter for frontend, FastAPI for backend services, MongoDB Atlas for data storage, and Firebase for authentication and real-time communication. Additionally, an AI-powered chatbot using Dialogflow ES assists users during onboarding and navigation [15]. Performance evaluation demonstrates that JobSwipe achieves a swipe latency of under 2.2 seconds and chatbot accuracy above 80%, meeting all defined non-functional requirements. The system proves that integrating intuitive interaction models with AI-driven assistance can significantly enhance recruitment efficiency and user satisfaction.