Design And Implementation Of Microblog Commodity Recommendation System Based On LDA

Weibo and online shopping malls,which embrace massive active users and large amount of information,cover a wide range of the crowd and the life,but most of the current business applications are based on recommend ation of Weibo itself and attention from acquaintances considering the characteristics of Weibo and online shopping malls,this paper aims to the design and implementation of the Weibo-products recommendation system.From the angle of practical commercial application,this paper,analyzing the value of commercial application between Weibo and Online mall,designs a product recommendation system based on Weibo,and finally explains how to make it and how the analysis comes.This paper researches on the vecto r of keywords from the public dataset of Weibo and gathered data of JD Mall and extracts those respectively by stutter segmentation tool,which defines the distribution range of each topic by Gibbs sampling of the characteristic data set which comes from J D products' information,recommendation of similar products is also established by LDA model and an increment LDA model realize the dynamic update in the light of the traits of dynamic change from Weibo and Online mall.Finally,Cosine similarity algorithm is used to calculate the similarity of the subject vectors,and in the LDA model,we attain the most similar 10 commodity information of each micro-blog,and recommend the corresponding goods to link the content of Weibo with the product information of the online mall to realize the products' recommendation of Weibo based on LDA.From the perspective of practical commercial applications,this paper have explored the maximization of the value between the Weibo platform and the online mall,and proposed a feasible solution with high application value and practical significance.