Clustering Analysis by Customer Feature based on SOM for Predicting Purchase Pattern in Recommendation System

조영성; Moon， song chul; RYU KEUN HO

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2014, 19(2), pp.193-200
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

조영성 ¹ , Moon， song chul ² , RYU KEUN HO ³

¹ 동양미래대학교
² 남서울대학교
³ 충북대학교

ABSTRACT

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.
KEYWORDS

Segmentation Method , SOM(Self-Organizing Map) , Recommendation System
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KCI Citation (2)

KJC Korea
Journal Central

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