Events Recommendation System

Computer Science and Engineering

The Event Recommendation System is a dynamic web-based platform developed to simplify the way students and institutions engage with events. Designed primarily for educational institutions, the system allows administrators to post and manage event details, while users (students) can browse, rate, and participate in events related to their own institutions. The platform provides a dedicated login for administrators, ensuring that only authorized personnel can create and modify events, while users access the content through a separate, secure user login. The website backend is developed using XAMPP, an open-source server solution that includes Apache, MySQL, PHP, and Perl. The backend handles the core functionalities like user authentication, event management, and secure data storage. Each institution is individually listed on the platform, enabling users to view events specific to their college or university, enhancing the relevance and user experience. A key feature of the system is its personalized event recommendation functionality, powered by a simple yet effective Machine Learning (ML) model. We have implemented the K-Nearest Neighbors (KNN) algorithm to analyze user activities — such as event clicks, ratings, and recent interactions — to understand their preferences and suggest events accordingly. This helps in providing a tailored experience for users, allowing them to discover events that match their interests without manually searching through all listings. The combination of a user-friendly website interface, efficient backend operations, and intelligent recommendation systems ensures a seamless interaction for both administrators and users. Overall, the project successfully demonstrates the integration of modern web development with machine learning techniques to build a useful, real-world application that could be scaled across various institutions.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.