COLLEGE FORUMS WITH ALUMNI BASED ON CONTENT FILTERING

Abstract: College forums serve as essential platforms for communication, knowledge sharing, and networking among students and alumni. However, traditional forums often lack personalization, leading to irrelevant content recommendations and limited engagement. To address this issue, a content-based filtering approach can be implemented to enhance interactions within college forums by recommending relevant discussions, resources, and alumni connections based on user preferences and past activities. By utilizing natural language processing (NLP) and machine learning techniques, the system can analyze forum content, user interests, and alumni expertise to provide personalized recommendations. This ensures that students are connected with alumni who share similar academic or career interests, fostering mentorship opportunities and career guidance. The content-filtering approach improves the relevance of forum discussions, leading to increased user engagement and more meaningful interactions. Additionally, it creates an adaptive learning environment where students receive tailored insights and support from experienced alumni. Future enhancements may involve hybrid filtering techniques, integrating collaborative filtering to further refine recommendation accuracy. This work contributes to the development of intelligent, personalized college forums that strengthen alumni networks and promote continuous learning and professional growth.

Keywords: College forums, alumni network, content-based filtering, personalized recommendations, Natural Language Processing (NLP), machine learning, mentorship, career guidance, user engagement, adaptive learning.