User Behavior Analysis Of Online Shopping Website Based On Analytic Hierarchy Process

`Through the development of information technology and e-commerce, people’slives have become more convenience than before, and online shopping websites havegreat development. From the year of2008, with the popularity of the network, therehas been a great development in both scope and scale. That has been an importantchallenge for many websites currently need to face, how the web dealing with onlineshopping development in the network environment，and provide better service thannow.When gests are browsing website or shopping online, the websites will get a lotof data about the gest, such as how long the gest spend in one web page, whichproduct the gest has view, the choice when they are face many different or similarproducts, what the gest are focus on the product in their counseling, and so on. In thebackground that data mining are widely used, using these massive data users leaved,websites can dig out users’ preferences, values, shopping patterns, etc., base what thatwebsite merchants can post advertising to guests, providing customers targetedservices, so that websites will have improved traffic, sales, and influence effectively.It has also become the hot point of many websites.This paper has analyzed website users’ behaviors when browsing websites andshopping online using data mining methods, aimed to improve online shoppingwebsites’ traffic, sales and influence. What this paper did as following:(1) Expounded AHP theory, and descripted calculation method and norm data indetail.(2) Analyzing general data flow of online shopping websites. During the websitesrequirements analysis, decomposition the functions whose completion need tofocus on and base what to complete the websites design. According uses’shopping process, designed the data transfer process of user information fromfront to backyard of the websites, then the data has been collected andanalyzed..(3) Creating model of users’ behaviors and analyzing it with propose ofimproving online shopping websites’ traffic, sales, and influence.