@article{ART001672624},
author={조영성 and 구미숙 and Ryu, Keun Ho},
title={Development of Personalized Recommendation System using RFM method and k-means Clustering},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2012},
volume={17},
number={6},
pages={163-172},
doi={}
TY - JOUR
AU - 조영성
AU - 구미숙
AU - Ryu, Keun Ho
TI - Development of Personalized Recommendation System using RFM method and k-means Clustering
JO - Journal of The Korea Society of Computer and Information
PY - 2012
VL - 17
IS - 6
PB - The Korean Society Of Computer And Information
SP - 163
EP - 172
SN - 1598-849X
AB - Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.
KW - RFM Method;Collaborative Filtering;Clustering;Recommend System
DO -
ER -
조영성, 구미숙 and Ryu, Keun Ho. (2012). Development of Personalized Recommendation System using RFM method and k-means Clustering. Journal of The Korea Society of Computer and Information, 17(6), 163-172.
조영성, 구미숙 and Ryu, Keun Ho. 2012, "Development of Personalized Recommendation System using RFM method and k-means Clustering", Journal of The Korea Society of Computer and Information, vol.17, no.6 pp.163-172. Available from: doi:
조영성, 구미숙, Ryu, Keun Ho "Development of Personalized Recommendation System using RFM method and k-means Clustering" Journal of The Korea Society of Computer and Information 17.6 pp.163-172 (2012) : 163.
조영성, 구미숙, Ryu, Keun Ho. Development of Personalized Recommendation System using RFM method and k-means Clustering. 2012; 17(6), 163-172. Available from: doi:
조영성, 구미숙 and Ryu, Keun Ho. "Development of Personalized Recommendation System using RFM method and k-means Clustering" Journal of The Korea Society of Computer and Information 17, no.6 (2012) : 163-172.doi:
조영성; 구미숙; Ryu, Keun Ho. Development of Personalized Recommendation System using RFM method and k-means Clustering. Journal of The Korea Society of Computer and Information, 17(6), 163-172. doi:
조영성; 구미숙; Ryu, Keun Ho. Development of Personalized Recommendation System using RFM method and k-means Clustering. Journal of The Korea Society of Computer and Information. 2012; 17(6) 163-172. doi:
조영성, 구미숙, Ryu, Keun Ho. Development of Personalized Recommendation System using RFM method and k-means Clustering. 2012; 17(6), 163-172. Available from: doi:
조영성, 구미숙 and Ryu, Keun Ho. "Development of Personalized Recommendation System using RFM method and k-means Clustering" Journal of The Korea Society of Computer and Information 17, no.6 (2012) : 163-172.doi:
