명시적 및 암시적 피드백을 활용한 그래프 컨볼루션 네트워크 기반 추천 시스템 개발

• 영문명: Developing a Graph Convolutional Network-based Recommender System Using Explicit and Implicit Feedback
• 발행기관: 한국IT서비스학회
• 저자명: 이흠철 김동언 이청용 김재경
• 간행물 정보: 『한국IT서비스학회지』제22권 제1호, 43~56쪽, 전체 14쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2023.02.28

영문 초록

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

목차

1. 서론

2. 관련 연구

3. OGCN 프레임워크

4. 실험

5. 결론

참고문헌

키워드

Graph Convolutional Network Recommender System Explicit and Implicit Feedback Customer-Product Interaction