Clustering Method Using Item Preference Based on RFM for Recommendation System in U-Commerce

  • Young Sung Cho
  • Song Chul Moon
  • Seon-phil Jeong
  • In-Bae Oh
  • Keun Ho Ryu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 214)


This paper proposes a new method using clustering of item preference based on Recency, Frequency, Monetary (RFM) for recommendation system in u-commerce under fixed mobile convergence service environment which is required by real time accessibility and agility. In this paper, using an 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 scoring of RFM to be able to reflect the attributes of the item and clustering in order to improve the accuracy of recommendation with high purchasability. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.


RFM Collaborative filtering Clustering 



This work (1) was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012-0000478) and this paper (2) was supported by funding of Namseoul University.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Young Sung Cho
    • 1
  • Song Chul Moon
    • 2
  • Seon-phil Jeong
    • 3
  • In-Bae Oh
    • 4
  • Keun Ho Ryu
    • 1
  1. 1.Department of Computer ScienceChungbuk National UniversityCheongjuKorea
  2. 2.Department of Computer ScienceNamseoul UniversityCheonan-cityKorea
  3. 3.Computer Science and Technology, DSTBNU-HKBU United International CollegeZhuhaiChina
  4. 4.Juseong UniversityChungbukKorea

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