Clustering Method Using Weighted Preference Based on RFM Score for Personalized Recommendation System in u-Commerce

  • Young Sung Cho
  • Song Chul Moon
  • Seon-phil Jeong
  • In-Bae Oh
  • Keun Ho Ryu
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 280)


This paper proposes a new clustering method using the weighted preference based on RFM(Recency, Frequency, Monetary) Score for personalized recommendation in u-commerce under ubiquitous computing 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, it is necessary for us to extract the most frequent purchase items from the whole purchase data and to calculate the weighted preference of item for customer in order to reduce customers’ search effort, to reflect frequently changing trends by emphasizing the important items and to improve the rate 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 Analysis Collaborative Filtering k-means Clustering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

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 TechnologyDST, BNU-HKBU United International CollegeZhuhaiP.R.China
  4. 4.Chungbuk Health & Science UniversityChungbukKorea

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