@article{ART001851439},
author={조영성 and Moon， song chul and Ryu, Keun Ho},
title={Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2014},
volume={19},
number={2},
pages={193-200}
TY - JOUR
AU - 조영성
AU - Moon， song chul
AU - Ryu, Keun Ho
TI - Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System
JO - Journal of The Korea Society of Computer and Information
PY - 2014
VL - 19
IS - 2
PB - The Korean Society Of Computer And Information
SP - 193
EP - 200
SN - 1598-849X
AB - Due to the advent of ubiquitous computing environment, it is becoming a part of our commonlife style. And tremendous information is cumulated rapidly. In these trends, it is becoming a veryimportant technology to find out exact information in a large data to present users. Collaborativefiltering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically usedto improve these defects. In this paper, we propose clustering method by user’s features based onSOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the clusterwith similarity by user’s features to be able to reflect attributes of the customer information inorder to find the items with same propensity in the cluster rapidly. The proposed makes the taskof clustering to apply the variable of featured vector for the user's information and RFM factorsbased on purchase history data. To verify improved performance of proposing system, we makeexperiments with dataset collected in a cosmetic internet shopping mall.
KW - Segmentation Method;SOM(Self-Organizing Map);Recommendation System
DO -
UR -
ER -
조영성, Moon， song chul and Ryu, Keun Ho. (2014). Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System. Journal of The Korea Society of Computer and Information, 19(2), 193-200.
조영성, Moon， song chul and Ryu, Keun Ho. 2014, "Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System", Journal of The Korea Society of Computer and Information, vol.19, no.2 pp.193-200.
조영성, Moon， song chul, Ryu, Keun Ho "Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System" Journal of The Korea Society of Computer and Information 19.2 pp.193-200 (2014) : 193.
조영성, Moon， song chul, Ryu, Keun Ho. Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System. 2014; 19(2), 193-200.
조영성, Moon， song chul and Ryu, Keun Ho. "Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System" Journal of The Korea Society of Computer and Information 19, no.2 (2014) : 193-200.
조영성; Moon， song chul; Ryu, Keun Ho. Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System. Journal of The Korea Society of Computer and Information, 19(2), 193-200.
조영성; Moon， song chul; Ryu, Keun Ho. Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System. Journal of The Korea Society of Computer and Information. 2014; 19(2) 193-200.
조영성, Moon， song chul, Ryu, Keun Ho. Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System. 2014; 19(2), 193-200.
조영성, Moon， song chul and Ryu, Keun Ho. "Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System" Journal of The Korea Society of Computer and Information 19, no.2 (2014) : 193-200.
