Implementation of Personalized Recommendation System using RFM method in Mobile Internet Environment
Young-Sung Cho, Moon-Haeng Huh, Keun-Ho Ryu · Journal of the Korea Society of Computer and Information · 2008
This paper proposes the recommendation system which is a new method using RFM method in mobile internet environment. Using a implict method which is not used user's profile for rating, is not used complicated query processing of the request and the response for rating, it is necessary for user to keep the RFM score about users and items based on the whole purchased data in order to recommend the items. As there are some problems which didn't exactly recommend the items with high purchasablity for new customer and new item that do not have the purchase history data. in existing recommendation systems, this proposing system is possible to solve existing problems, and also this system can avoid the duplicated recommendation by the cross comparison with the purchase history data. It can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic cyber shopping mall. Finally, it is able to realize the personalized recommendation system with high purchasablity for one to one web marketing through the mobile internet.