Neural Network-Based Hybrid Recommendation System

The main goal of this study is to improve the user experience by providing personalized product recommendations based on user preferences and behaviors. The study examines the various methods and frameworks commonly used in e-commerce recommendation processes, including collaborative filtering, content filtering, and using these methods in neural networks system. Additionally, the proposed algorithm solves the “cold start problem” by newly visited users. Results obtained by proposed approach that use popularity-based algorithm to create the initial set of recommendations highlights how effective the system is at providing accurate and relevant recommendations to users, ultimately increasing customer satisfaction and sales conversion rates of supply. These findings and insights contribute to the development of e-commerce recommendation systems, and present possible avenues for future research and development.