CONTENT AND ACTIVITY BASED FRIENDS SUGGESTION IN AN ANNOTATION SYSTEM

Abstract

With the increasing use of annotation systems for content organization and learning, there is a need for enhanced user interaction. This project aims to develop a system for content and activity-based friend suggestions within an annotation framework. Users sign up, use a browser extension to save annotations, add comments to selected text, and save these annotations. The system recommends relevant content, allows users to follow others, like, and save notes, and enables keyword extraction and testing. Based on performance, it suggests YouTube videos and highlights top users. Users can search for and chat with others, promoting an enriched and collaborative learning experience.