Earticle

초록

영어

Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer’s purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer’s purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

목차

Abstract
 1. 서론
 2. 제안 추천 방법론
  2.1 학습과정 : GBN 과 CBNu 의 구성
  2.2 적용과정 : EBNu 구성 및 추천 브랜드 도출
 3. 실험
  3.1 실험 데이터
  3.2 분석 방법
  3.3 분석 결과
 4. 결론
 References

키워드

저자

• 정석봉 [ Seok Bong Jeong | Professor, Department of Railway, Kyungil University, Kyungil University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005