Earticle

초록

영어

This paper proposes a recommender system based on periodicity analysis from purchase history data using mining sequential pattern with time-series and FRAT (Frequency, Regency, Amount and Type of merchandise or service) analysis. In this paper, not used user's profile for rating, it is necessary for us to make the task of mining sequential pattern with time-series and clustering of category with weight using FRAT analysis in order to recommend service by frequently changing trends of purchase pattern. For doing that, it is considered the importance of type of merchandise or service and then, we suggest recommending method based on periodicity analysis to reflect frequently changing trends of seasonable purchase pattern for the four seasons as time goes by and as often as customers need different merchandises on e-commerce being extremely diverse. We evaluated the proposing system on the data set collected in the same dataset collected on a cosmetic internet shop to measure its performance. We reported some of the experimental result, in the same condition, which is better performance than both the previous system and existing system.

목차

Abstract
 I. INTRODUCTION
 II. RELATED WORKS
 III. OUR PROPOSAL FOR RECOMMENDER SYSTEM IN E-COMMERCE
 IV. THE ENVIRONMENT OF IMPLEMENTATION AND EXPERIMENT & EVALUATION
 V. CONCLUSION
 ACKNOWLEDGMENT.
 REFERENCES

키워드

저자

• Young Sung Cho [ Dept. of Computer Science, Chungbuk National University, Cheongju, South Korea ]
• Song Chul Moon [ Dept. of Computer Science, Namseoul University, Cheonan, South Korea ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JoC [Journal of Convergence]

• 간기

  계간

• pISSN

  2093-7741

• eISSN

  2093-775X

• 수록기간

  2010~2015

• 십진분류

  KDC 004 DDC 004