Design And Implementation Of Personalized Recommendation System For E-Commerce Platform

With the rapid development of Internet industry,online shopping has become one of the essential shopping methods in people's daily life,and the problem of information overload is particularly prominent.In order to deal with this problem,the major Internet companies have built their own personalized recommendation system to solve it,while small and medium-sized e-commerce companies do not have a better solution.This paper hopes to analyze the personalized recommendation algorithm of current e-commerce platform,find optimization points,optimize in data collection,data analysis,algorithm,operation tools,and finally complete the personalized recommendation system with good effect.This paper introduces the research background,purpose and significance of personalized recommendation algorithm and recommendation system based on deep learning.Based on the research of the mature XNN algorithm model and the collection and demand analysis of the user data of the e-commerce platform,the optimization scheme of the personalized recommendation algorithm of the e-commerce platform and the feasibility solution of the system are given.This paper designs and implements a personalized recommendation system based on user behavior by referring to the structure of the existing personalized recommendation system.This paper optimizes the algorithm based on XNN algorithm,verifies the recommendation effect and feasibility of the optimized FNN algorithm,and completes the design and implementation of the background of personalized recommendation system,so as to help the e-commerce platform to complete personalized recommendation and monitor the recommendation effect in real time.