Earticle

As the E-commerce market grows, the importance of personalized recommendation systems is increasing. Existing collaborative filtering and content-based filtering methods have shown a certain level of performance, but they have limitations such as cold start, data sparseness, and lack of long-term pattern learning. In this study, we design a matching system that combines a hybrid recommendation system and hyper-personalization technology and propose an efficient recommendation system. The core of the study is to develop a recommendation model that can improve recommendation accuracy and increase user satisfaction compared to existing systems. The proposed elements are as follows. First, the hybrid-hyper-personalization matching system provides recommendation accuracy compared to existing methods. Second, we propose an optimal product matching model that reflects user context using real-time data. Third, we optimize Personalized Recommendation System using deep learning and reinforcement learning. Fourth, we present a method to objectively evaluate recommendation performance through A/B testing.