Personalized Recommendation System Using Retrieval-Augmented Generation (RAG) and Knowledge Graphs

Project description

This project aims to develop a personalized recommendation system that leverages the strengths of Retrieval-Augmented Generation (RAG) and knowledge graphs. The system will integrate RAG to enhance the retrieval of relevant user-specific information while utilizing knowledge graphs to represent and infer relationships between users, items, and contextual factors. By combining these techniques, the project seeks to provide more accurate and context-aware recommendations tailored to individual preferences and needs. The implementation will focus on designing a robust pipeline that blends semantic understanding from knowledge graphs with RAG's dynamic retrieval and generation capabilities, offering a scalable and adaptive recommendation system for various application domains.

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