Research And Implementation Of E-commerce Personalized Recommendation System Based On Collaborative Filtering Algorithm

At present,the popularity of the Internet has greatly promoted the rapid development of e-commerce,deepening the potential needs of users,a nd timely pushing the products of interest to become a label for an excellent e-commerce platform.However,most of the existing e-commerce recommendation systems mainly rely on user ratings as the standard for user product recommendation,but users often do not want to perform manual scoring operations;therefore,existing recommendation systems have insufficient data analysis and chromatography,and the recommendation method is single.,recommended low efficiency and many other deficiencies.In view of the related problems in the e-commerce recommendation system,this paper proposes an e-commerce personalized recommendation system based on collaborative filtering algorithm,which partially solves the above problems.The main work of this paper is as follo ws:1.In view of the shortcomings of the current e-commerce personalized recommendation,comprehensive analysis of the target requirements such as improving the recommendation accuracy;clearing the personalized recommendation business process,and dividing it into four user roles,using the use case table for each role's permissions and coverage The business process is explained.Subsequently,the functional requirements of the recommendation system are further analyzed,and it is divided into five core functional blocks,such as user module,sales module,recommendation module,commodity module and system module,and elaborated.Further analyze the non-functional requirements of the system to ensure that the system is easy to use,safe and reliable,and t he interface is beautiful.2.Design and implement an e-commerce personalized recommendation system based on collaborative filtering algorithm.In order to fully exploit the user's implicit information,in order to recommend personalized products that bett er meet the needs,this paper utilizes and analyzes the user's browsing log,and designs the browsing log pre-processing flow to correct the error information in the browsing log and extract the user's interest.And converting it into an input parameter of the collaborative filtering algorithm,inputting into the collaborative filtering recommendation algorithm,and generating a candidate recommendation result for the user.3.The system performance is tested separately.The results show that the system designed in this paper is complete in function and can meet the requirements of certain concurrent access and recommendation.The system can meet multiple functional and non-functional requirements,and in the case of a concurrent number of 100,the system ca n support 160,000 users in normal use within 8 hours.In addition,the system indicators are in a stable state after multiple functional tests.This paper researches and develops the actual needs of the current e-commerce personalized recommendation system,designs and implements an e-commerce personalized recommendation system,and has entered the trial operation stage.The operation shows that the system can fully exploit the implicit information in the user activity log,solve the problem that the user's behavior log lacks negative feedback,increase the level of data analysis and analysis,and strengthen the personalization of user recommendation.Our system provides a good reference value for the development of related personalized e-commerce recommendation systems.