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A Personalized Recommendation System for e-Supply Chain Based on Improved FAT-Growth Algorithm

Huang Li-juan

Abstract


In the Internet era of personalized publicity, single layout of traditional "brick-and-mortar" stores can hardly satisfy all consumers? tastes, and it will let the stores lose many potential business chances. There is a large sum of data about consumers? personalized behavior in e-supply chain, and the author makes full use of these valuable data to design Personalized Recommendation Systems (PRS) for every consumer online to increase more business chances than the traditional ones do. FAT-growth algorithm is a new and interesting apriori algorithm, but there are still two obvious deficiencies in PRS design due to too giant data in e-supply chain to process. This paper takes personalized Internet store for example to improve the algorithm, and adopt the improved one to construct PRS, which can not only help to realize one-to-one marketing strategies and increase the loyalty of consumers to the Internet store, but also reach a win-win game balance between e-supply chain and the consumers. The experiment shows that the design can effectively improve scalability and accuracy of the e-supply chain?s PRS.


Keywords


Algorithm, Internet, supply chain, PRS.

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