Earticle

초록

영어

Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

목차

Abstract
 1. 서론
 2. 연구배경
  2.1 기존 추천기법의 한계점
  2.2 기존 시장세분화 방법의 한계점
 3. 연구내용 및 방법
 4. 실험데이터와 실험설계
  4.1 실험데이터
  4.2 실험 설계
 5. 실험결과
 6. 결론
 참고문헌

키워드

저자

• 김경재 [ Kyoung-Jae Kim | 동국대학교 경영대학 정보관리학과 ] 주저자
• 안현철 [ Hyunchul Ahn | 한국과학기술원 테크노경영대학원 ]

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