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

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of ∙제1저자 : 조영성 ∙교신저자 : 문송철 ∙투고일 : 2013. 10.24, 심사일 : 2013. 11. 20, 게재확정일 : 2013.12. 3. * 동양미래대학교 전산정보학부(Dept. of Computer Science, Dongyang mirae University) ** 남서울대학교 컴퓨터학과(Dept. of Computer Science, Namseoul University) *** 충북대학교 전자컴퓨터공학부(School of Electrical and Computer Science, Chungbuk National University), 194 Journal of The Korea Society of Computer and Information February 2014 user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user’s features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user’s features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall. ▸