DEVELOPMENT OF BOOK RESOURCES RECOMMENDATION SYSTEM USING LLMS AND HYBRID COLLABORATIVE FILTERING.

A recommender system is an Information Retrieval technology that improves access and proactively recommends relevant items to users by considering the users explicitly mentioned preferences and objective behaviors. A recommender system is one of the major techniques that handle information overload problem of Information Retrieval by suggesting users with appropriate and relevant items. This project presents the design and implementation of a hybrid AI-driven recommendation system focused on delivering personalized educational content. The system combines content-based and collaborative filtering techniques to address common limitations such as cold-start problems, overspecialization, and data sparsity. By leveraging Large Language Models (LLMs), the system generates semantic representations of both learning materials and user preferences, enabling more accurate and context-aware recommendations. The aim is to enhance learners’ experiences by providing relevant and diverse study resources tailored to their needs and behavior. This approach offers a scalable, intelligent solution for improving educational content delivery.