Leveraging Machine Learning to Enhance Student Engagement in Campus Applications

Abstract: Access to essential services is a vital need for students who migrate to other places. While access to essential services is crucial for students relocating, simply providing access falls short of fostering a sense of belonging. Existing platforms lack localized information as well as personalization. This paper proposes an open platform that streamlines access to campus services while offering a personalized experience through machine learning. The platform streamlines access to services such as housing, dining, and marketplace while providing a personalized experience. The platform provides an interface for both students and service providers, allowing providers to gain insights and improve their businesses as well. The proposed system tackles common drawbacks faced by existing recommendation systems by employing a hybrid recommender system. The system follows a service-oriented architecture developed using microservice architecture, which allows services to be independent and makes the platform scalable. The platform addresses limitations of traditional recommendation systems by utilizing a hybrid approach, which results in better accuracy in recommendations than existing systems.

Keywords: Campus Services, Machine Learning, Recommendation systems, hybrid recommendation, personalization, open platform, scalability, service-oriented architecture.