Personalized Recommendation Based On User Behavior Of An E-commerce Platform

At present,the scale of Internet data is rapidly expanding.We are already sitting on massive amounts of information.The efficiency of finding useful information for ourselves has become lower and lower.At present,a major problem facing e-commerce platforms is how to quickly and accurately obtain a large number of products.From the information,the products that the user is interested in are filtered out and presented to the user.The personalized recommendation service is a powerful tool to deal with this problem.It can not only provide users with high-quality services,but also bring unprecedented profits to businesses.The user behavior in the e-commerce platform is meaningful,and it can even be said that every user's behavior operation reflects the essential needs of the user's heart.Therefore,this article analyzes the user behavior of Ali platform in terms of time evolution,behavior conversion,behavior time interval,and repurchase situation,and finds that the user churn rate after browsing is high and the purchase conversion rate is low,as well as some other user behavior characteristics.In order to meet the personalized needs of different users,improve the user's shopping experience,reduce user churn,increase purchase conversion rate,and create greater value for merchants,this article conducts research on personalized recommendations for e-commerce platforms.This article introduces three commonly used recommendation methods,compares and analyzes their advantages and disadvantages and their applicable scenarios.It is found that content-based recommendation methods are more suitable for text recommendation fields,and recommendation methods based on association rules are mainly used to discover shopping carts.The relevance between collaborative filtering methods is highly personalized,and the potential needs of users can be explored,and the interpretability is strong.For the e-commerce platform,in order to complete the user's personalized recommendation based on user behavior data,this paper finally chooses the user-based collaborative filtering recommendation method.But at the same time,it is also found that the collaborative filtering recommendation algorithm has data sparseness problems,cold start problems and scalability problems.In this regard,this article combines the collaborative filtering recommendation algorithm with the k-means clustering algorithm for product recommendation,and compares it with the traditional collaborative filtering recommendation algorithm.Experimental results show that for e-commerce platforms,the accuracy,recall,and F1 value of the collaborative filtering recommendation algorithm based on kmeans clustering are better than those of the traditional collaborative filtering recommendation algorithm,and its computational complexity is also lower.While alleviating the problem of data sparsity,it also effectively solves the problem of scalability.It shows greater advantages in both recommendation performance and recommendation efficiency,which provides a certain reference for the personalized recommendation service of e-commerce platforms.