Earticle

초록

영어

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.

목차

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

키워드

저자

• Sung-Ug Lee [ Department of Game Engineering, Tongmyong National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  컴퓨터게임및콘텐츠논문지(구 한국컴퓨터게임학회논문지) [Journal of Computer Games and Contents]

• 간기

  월간

• pISSN

  3091-7409

• eISSN

  3092-3638

• 수록기간

  2002~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 691 DDC 793