在线零售站点的自适应和商业智能的发现

There are two important problems in online retail:1)The conflict between the different interest of all customers to the different commodities and the commodity classification structure of Web site;2)Many customers will simultaneously buy both the beer and the diaper that are classified in different classes and levels in the Web site,which is the typical problem in data mining.The two problems will make majority customers access overabundant Web pages.To sove these problems,we mine the Web page data,server data,and marketing data to build an adaptive model.In this model,the frequently purchased commodities and their association commodity sets that are discovered by the association rule discovery will be put into the suitable Web page according to the placing method and the backing off method.At last the navigation Web pages become the navigation content Web pages.The Web site can be adaptive according to the users‘‘‘‘‘‘‘‘accesa and purchase information.In online retail,the designers require to understand the latent users‘‘‘‘‘‘‘‘interest in order to convert the latent users to purchase users.In this paper,we give the approach to discover the Internet marketing intelligence through OLAP in order to help the designers to improve their service.

Adaptive Online Retail Web Site and Discovering Internet Marketing Intelligence