The Design And Implementation Of Persionalized Recommendation System For E-commerce Platform

Nowadays, the competitions in the field of E-business are becoming much moreintense. Shopping platform which is the main support component for the E-business, isfacing more and more challenges. In the era of information explosion, in order toimprove the satisfactions of users, to achieve a higher users’ loyalty, lots ofE-commerce companies are focusing on the researches of personalizedrecommendation for the shopping platform, which could help customers finding thecomedies easily, and enhancing the shopping experience.Due to the problem of users’ experience people shopping on the E-commercialplatform, this thesis introduced a personalized recommendation system. This programdiscussed the design and implementations of the personalized recommendation system,which is used on the E-commerce platform thoroughly, by analyzing its businessrequirements and functional requirements. To be more specifically, the thesis discussedthe design and implementations of the input preparation module, the personalizedrecommendation engine module, the algorithms results store module, scenarioconfiguration module and finally recommendation’s responding module. Meanwhile,the thesis using Map-Reduce programming model specifically described the design andimplements of heat conducting algorithm, self projection algorithm and k-meansclustering algorithms.This program conducted the data mining using large scale amount of users’behavior data and commodities data. The system could support customers’ interestsand underlying preferences prediction, ensure the response speed and accuracy,manage data more efficiently as well. The personalized recommendation systemdiscussed in the thesis also gives the basic data analyzing support for the other businessrelated system. The system now is on the service in the alibaba.com, helping improvethe clicking rate, buying rate of the commodities, and improve the user’s shoppingsatisfaction.