한국경영과학회:학술대회논문집 (Proceedings of the Korean Operations and Management Science Society Conference)
- 대한산업공학회/한국경영과학회 2002년도 춘계공동학술대회
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- Pages.312-319
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- 2002
A Personalized Recommender based on Collaborative Filtering and Association Rule Mining
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Kim Jae Kyeong
(School of Business Administration, KyungHee University) ;
- Suh Ji Hae (School of Business Administration, KyungHee University) ;
- Cho Yoon Ho (Department of Internet Information Systems, Dongyang Technical College) ;
- Ahn Do Hyun (School of Business Administration, KyungHee University)
- 발행 : 2002.05.01
초록
A recommendation system tracks past action of a group of users to make a recommendation to individual members of the group. The computer-mediated marking and commerce have grown rapidly nowadays so the concerns about various recommendation procedure are increasing. We introduce a recommendation methodology by which Korean department store suggests products and services to their customers. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is to select target customers, who have high purchase possibility of recommended products. Product taxonomy and association rule mining are used to select proper products. The validity of our recommendation methodology is discussed with the analysis of a real Korean department store.
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