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http://dx.doi.org/10.9708/jksci.2012.17.2.197

Personalized Recommendation System using FP-tree Mining based on RFM  

Cho, Young-Sung (School of Computer Science & Information, DongYang Mirae University)
Ho, Ryu-Keun (School of Electrical & Computer Enginnering, Chungbuk National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.17, no.2, 2012 , pp. 197-206 More about this Journal
Abstract
A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.
Keywords
RFM Method; FP-tree Mining; Recommendation System;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
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