Today in the 21st century environment, due to the popularity of computers and the social economic recovery, online shopping is getting that trend. In the traditional marketing system, almost every 10-20 years, will have new marketing system, to become a new method of selling product to the end of consumer.
So, in the beginning of the 1990s, the prevalence of the Internet and e-commerce, so far, due to advances in technology, the popularity of computers and consumer demands of shopping convenience, and create the called”home economic”.
Therefore, the online shopping market is increasingly prosperous development, while many consumers tend to price low, the convenience and security of the trading mechanism, therefore, the online shopping platform is not high consumer loyalty, in order to attract a wider consumer to purchase goods, and help enterprises significant increase in the website traffic and improve the network platform of consumer loyalty, this study will design a personalized recommendation mechanism, providing consumers with more refined shopping environment and service.
In this study, a combination of rough sets and data mining of association rules, focus on the rules of dealing with uncertainty information generated to help the marketing decision-makers can accurately segment the market and the development of the rough-set based association rule algorithm, together with the relative weight of the concept of the Analytic Hierarchy Process (AHP), the establishment of external and referral mechanism will be the appropriate product and platform path recommended to consumers, the end to see if it can change their consumption behavior.