Design a personalized recommendation system using deep learning and reinforcement learning

• 발행기관: 한국컴퓨터게임학회
• 저자명: Sung-Ug Lee
• 간행물 정보: 『한국컴퓨터게임학회논문지』제38권 1호, 45~56쪽, 전체 12쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2025.03.31

국문 초록

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

1. Introduction

2. Matching System Design

3. Matching System Architecture

4. Analyze the case implementation

5. Conclusion and Future Research Directions

참고문헌

키워드

collaborative filtering(CF) content-based filtering(CBF) hybrid recommendation system Personalized Recommendation System A/B testing Neural Collaborative Filtering (NCF) Reinforcement earning-Based(RL-based) Upper Confidence Bound(UCB)