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A Personalized Recommendation Methodology based on Collaborative Filtering
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Jae-Kyeong Kim (School of Business Administration, KyungHee Unversity)
Ji-Hae Suh (School of Business Administration, KyungHee Unversity)
Do-Hyun Ahn (School of Business Administration, KyungHee Unversity)
Yoon-Ho Cho (Department of Internet Information, Dongyang Technical College)
Vol. 8, No. 2, Page: 139 ~ 157
Keywords
Recommendation system, Personalization, Collaborative filtering, Decision Tree, Data Mining
Abstract
The rapid growth of e-commerce has made both companies and customers face a new situation. Whereas companies have become to be harder to survive due to more and more competitions, the opportunity for customers to choose among more and more products has increased. So, the recommender systems that recommend suitable products to the customer have an important position in E-commerce. This research introduces collaborative filtering based recommender system which helps customers find the products they would like to purchase by producing a list of top-N recommended products. The suggested methodology is based on decision tree, product taxonomy, and association rule mining. Decision tree is used to select target customers, who have high possibility of purchasing recommended products. We applied the recommender system to a Korean department store. The methodology is evaluated with the analysis of a real department store case and is compared with other methodologies.
Show/Hide Detailed Information in Korean
협업 필터링 기법을 활용한 개인화된 상품 추천 방법론 개발에 관한 연구
김재경 (경희대학교 경영학부)
서지혜 (경희대학교 경영학부)
안도현 (경희대학교 경영학부)
조윤호 (동양공업전문대학 인터넷정보과)
Abstract
본 연구에서는 기존 협업 필터링의 문제점을 해결할 수 있는 효율적인 상품추천 방법론을 제시하고자 한다. 연구에서 제시하는 상품추천 방법론은 기존 협업 필터링 알고리즘의 데이터 희박성 문제 및 동의어 문제를 극복하기 위하여 판매 데이터로 구성된 제품 계층도(Product Taxonomy)를 이용하며, 이 계층도를 기반으로 한 연관 규칙(association rule)과 의사결정 나무를 사용한다. 본 연구에서는 제시한 방법론을 단계별로 설명하였을 뿐만 아니라, 실제 H 백화점 데이터를 이용하여 적용하였다. 다양한 경우에 대하여 실험을 한 결과, 기존의 협업 필터링 알고리즘이 갖고있는 문제점을 상당히 해결하였음을 제시하였다. 이 연구에서 제시한 상품 추천 방법론은 현재 기업이 직면한 경쟁환경 하에서 고객이 과연 누구이며, 고객이 진정 무엇을 원하고 있는지를 파악하는데 도움을 줄 것이며, 고객관계관리 (CRM)를 효율적으로 구현하는 방법론으로 사용될 것으로 기대된다.
Cite this article
JIIS Style
Kim, J.-K., J.-H. Suh, D.-H. Ahn, and Y.-H. Cho, "A Personalized Recommendation Methodology based on Collaborative Filtering", Journal of Intelligence and Information Systems, Vol. 8, No. 2 (2002), 139~157.

IEEE Style
Jae-Kyeong Kim, Ji-Hae Suh, Do-Hyun Ahn, and Yoon-Ho Cho, "A Personalized Recommendation Methodology based on Collaborative Filtering", Journal of Intelligence and Information Systems, vol. 8, no. 2, pp. 139~157, 2002.

ACM Style
Kim, J.-K., Suh, J.-H., Ahn, D.-H., and Cho, Y.-H., 2002. A Personalized Recommendation Methodology based on Collaborative Filtering. Journal of Intelligence and Information Systems. 8, 2, 139--157.
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@article{Kim:JIIS:2002:143,
author = {Kim, Jae-Kyeong and Suh, Ji-Hae and Ahn, Do-Hyun and Cho, Yoon-Ho},
title = {A Personalized Recommendation Methodology based on Collaborative Filtering},
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 2002},
volume = {8},
number = {2},
month = Dec,
year = {2002},
issn = {2288-4866},
pages = {139--157},
url = {},
doi = {},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Recommendation system, Personalization, Collaborative filtering, Decision Tree and Data Mining },
}
%0 Journal Article
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%A Jae-Kyeong Kim
%A Ji-Hae Suh
%A Do-Hyun Ahn
%A Yoon-Ho Cho
%T A Personalized Recommendation Methodology based on Collaborative Filtering
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 8
%N 2
%P 139-157
%D 2002
%R
%I Korea Intelligent Information System Society