원문정보
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
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