RecMe: A Personal Hybrid Recommendation System

—The growing need for accurate recommendations has motivated the devel opment of hybrid recommendation systems. In this project, we propose a sys tem that integrates collaborative filtering, content-based filtering, and knowledge graph approaches to enhance recommendation accuracy and address limitations such as cold-start and data sparsity. The system uses a multi-step method: col laborative filtering predicts user preferences based on similar users, content-based filtering analyses item attributes, and a knowledge graph refines recommendations by leveraging relational data. The hybrid approach offers advantages like more personalized recommendations and better handling of sparse data. However, it has the drawback of increased computational complexity due to integrating multiple algorithms. In conclusion, the system aims to effectively combines the strengths of different recommendation techniques, to provide more precise and relevant suggestions to users.