Encouraging Recycling in Bangi Selatan Through a Content-Based Filtering Web Application

This study addresses the challenges faced by residents of Bangi Selatan in adopting 3R (Reduce, Reuse, Recycle) practices, primarily due to a lack of interest in conservation efforts and insufficient awareness of recycling’s importance. To address these challenges, we presented a web application that enhances recycling adoption by delivering personalized content recommendations. The key contributions of this study include the development of a novel recommendation system based on content-based filtering (CBF) with improved accuracy through a modified Term Frequency-Inverse Document Frequency (TF-IDF) formula. We compare various recommendation techniques, including collaborative and hybrid filtering, and demonstrate how CBF effectively improves user engagement with recycling content. Our methodology involves advanced text vectorization and cosine similarity for precise content matching. User acceptance testing confirms the system’s effectiveness in increasing user engagement with relevant recycling information. This study highlights the potential of personalized recommendation systems in promoting environmental conservation and provides a foundation for future enhancements in recycling initiatives.