The Key Technology And System Implementation Of Mobile Shopping Guide

The rapid development of mobile Web makes mobile shopping to be possible, users can acquire store information and merchandise information they need via the mobile terminal,and also can navigate through the terminal to meet user’s need to purchase goods of detailed location, so as to enhance the customer shopping experience.Compared with traditional shopping, mobile shopping can not only help users meet their needs from the mass of commodity information,but also make their lives more convenient, saving time, effort and money,and also has entertainment features.Recommended system is good or bad will directly influence the loyalty of users of the system, how to make it friendly for user to go shopping,and how to make diverse customized recommendation in the system,and how to improve the efficiency of recommending system,are the important subject of this paper.In this paper,we use the recommendation based on location to enable users to locate the store around quickly, then find out the store meetting their shopping needs from the surrounding stores. While mobile shopping guide system(MSGS) is to provide users with personalized recommendation service(guessing your like and similar users buying), good store recommendation, bestsellers recommendation, recommendation based on geographic location,which good store recommendation is calculated from the sale number of the store goods and commodity rating, bestsellers is the commodity with higher rating, recommendation based on geographic location recommends the sotres around according to the current location of the user,and the personalized recommendation is the focus of this paper.This paper is focuses on personalized recommendation algorithm of MSGS,and has proposed the FP-GROWTH recommendation algorithms based on rating rules and the K-Means collaborative filtering recommendation algorithm with dimensionality reduction and centroid selection based on Krusal,then analyzes the algorithms and compares the algorithms with other algorithm. Experimental results show that the propsed algorithms in this paper has achieved good results. Then this paper give out the needs analysis, the detailed design, the database design of MSGS,and so on,Then has finished the APP-side and server-side of MSGS.