Earticle

초록

영어

This paper proposes a new incremental weighted mining based on RFM((Recency, Frequency, Monetary) analysis for recommending prediction in u-commerce. Association rules search for the associated item set on large database. Association rules are frequently used by the marketing pattern analysis in e-commerce, recommendation to promote for selling a product in marketing. The proposing method can extract frequent items and create weighted association rules using incremental weighted mining based on RFM analysis rapidly when new data are added persistently in order to predict frequently changing trends by emphasizing the important items with high purchasability according to the threshold for creative weighted association rules in u-commerce. 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.

목차

Abstract
 1. Introduction
 2. Relative Works
  2.1. RFM Analysis
  2.2. Weighted Association Rules
  2.3. Mining using FP-tree
  2.4. Incremental Mining
 3. Our Proposal for Recommending Prediction in u-commerce
  3.1. System Architecture
  3.2. Incremental Weighted Mining based on RFM Analysis
  3.3. The Procedural Algorithm for Recommendation
  3.4. The Analysis of Application for Weighted Mining for Recommendation
 4. The Environment of Implementation and Experiment & Evaluation
  4.1. Experimental Environment
  4.2. Experimental Data for Evaluation
  4.3. Experiment & Evaluation
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Young Sung Cho [ Department of Computer Science, Chungbuk National University, Cheongju, Korea ]
• Song Chul Moon [ Department of Computer Science, Namseoul University, Cheonan-city, Korea, Korea ]
• In-Bae Oh [ Department of Industry Management, Chungbuk Health & Science University, Chungbuk, Korea ]
• Jung-Hoon Shin [ Department of Software Engineering, Chonbuk National University, Jeonju, Korea ]
• Keun Ho Ryu [ Department of Computer Science, Chungbuk National University, Cheongju, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605