Implementation of personalized recommendation system using k-means clustering of item category based on RFM

This paper proposes the recommendation system which is a new method using k-means clustering of item category based on RFM(Recency, Frequency, Monetary) in u-commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using a implicit method without onerous question and answer to the users, not used user´s profile for rating to reduce customers´ search effort, it is necessary for us to keep the analysis of RFM to be able to reflect the attributes of the item in order to find out the items with high purchasability. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.