Research On Campus E-commerce Personalized Recommendation Based On Association Rules

With the rapid development of Internet and e-commerce,the problem of information overload is getting more and more serious.So it is difficult for people to find out the information they need from large amount of information,and even problems of information trek appeared,the personalized recommendation technology arises at the historic moment.It is a key point in the research of e-commerce.The campus e-commerce has advanced as rapid as development of e-commerce in recent years.And the personalized recommendation technology has applied to campus e-commerce this special field,it helps businesses to find potential customers and change them into actual customers.It also helps users to find goods and services they need quickly and efficiently.This thesis focuses on the campus e-commerce personalized recommendation based on association rules.First of all,the concept of data mining and data mining technology are presented and summarized in detail in this paper.Then it analyses the personalized recommendation theory and e-commerce personalized recommendation technology carefully.And then,this paper introduces the basic theory related to association rules and association rules algorithm.Finds two obvious deficiencies by researching into the classical association rules algorithm-Apriori algorithm: it must scan the transaction database many times and it is to generate a large number of candidate item sets.Thus it is good to improve connection and pruning two steps to solve the above problems and put forward the improved algorithm of Apriori algorithm.Finally,relying on a campus e-commerce sites,we proposed a personalized recommendation model based on association rules,the proposed model is tested and the simulation results are analyzed.The aim of this paper is to put forward a suggestion that helps the campus e-commerce promote personalized recommendation service,improve the product conversion rate and find potential customers.