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.

ABSTRACT
1. Introduction
2. Matching System Design
    2.1 Matching system configuration
    2.2 System Architecture Overview
3. Matching System Architecture
    3.1 The Role of Hybrid Recommender
    3.2 The Role of Hyper-Personalized Recommendation Systems
    3.3 Hybrid-hyper-personalized matching system architecture
4. Analyze the case implementation
    4.1 Case study through implementation model
    4.2 RL-based Recommendation System
    4.3 Performance Evaluation Based on A/B Testing
5. Conclusion and Future Research Directions
참고문헌
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• Sungl-Ug Lee(Department of Game Engineering, Tongmyong National University) | 이승욱 Corresponding author