Unified Service Hub: A Dynamic Preference-Aware Service Matching Platform Using Adaptive Weighted Ranking

Digital service platforms have transformed the way users access professional and household services. However, many current systems depend heavily on static filtering mechanisms such as category, price, or location, which often fail to deliver the most relevant service options. This limitation increases user effort, delays decision-making, and reduces overall platform efficiency. This paper presents Unified Service Hub, a web-based intelligent service matching platform designed to improve service discovery through an adaptive weighted ranking framework. Unlike conventional filtering systems, the proposed platform evaluates services using multiple dynamic parameters including provider rating, service availability, response time, provider experience, user preference, and feedback-driven trust scoring. A dynamic ranking mechanism adjusts parameter weights based on user intent, enabling personalized and context-sensitive service recommendations. The system is implemented using a React-based Single Page Application frontend, a Spring Boot RESTful backend, and a MySQL relational database. To improve responsiveness and scalability, frontend optimizations such as lazy loading, caching, and paginated service retrieval are integrated into the architecture. Experimental evaluation indicates that the proposed system improves relevance in service selection, reduces user navigation effort, and creates a more adaptive and explainable recommendation process. The proposed framework establishes a practical foundation for future AI-driven recommendation systems in digital service marketplaces.