Research And Implementation Of Personalized Recommended Online Bookstore

With the advancement of computer technology and security,e-commerce has expanded into military,education,and medical fields.The number of goods available for selection on the Internet is huge.However,the ever-increasing scale of the Internet has caused major e-commerce websites to handle a large amount of content and provide a large number of results in response to user queries.As a result,the user cannot filter out unrelated product information,which causes a problem of information overload for the user.Businesses are increasingly aware that in today's rapidly changing business environment,it is critical to meet customer needs in the most efficient and timely manner.In order to help users find information that meets their interests more quickly and accurately,the recommendation system has emerged.The recommendation system tracks the user's browsing behavior and uses appropriate recommendation algorithms to predict the products that the user is interested in.In the research of the recommendation system,the determination of the recommendation algorithm and the accuracy of user information acquisition are the key issues in the study.In order to follow the trends and trends of e-commerce,this article has developed a personalized recommendation online bookstore system under actual demand,and has realized a professional e-commerce website that integrates personalized recommendation and online bookstore.The work done in this paper is as follows:1)To study the multiple technologies used in recommendation systems and web development,starting from the research background of the relevant domestic and foreign technologies of the recommendation system,content-based filtering,memory-based collaborative filtering,model-based collaborative filtering,and clustering-based Collaborative filtering recommendation algorithm is explained in detail;then the Web development technology and development process used in system development are elaborated.2)For sparseness problems encountered in collaborative filtering,the causes of sparseness problems and the impact on the recommender system are described.The evaluation indicators are used to evaluate existing sparseness-resolving algorithms.3)After processing the data of the book-Crossing data set,compare the collaborative filtering algorithm with the k-means algorithm based on the accuracy and recall rate to illustrate that the recommended performance is better after the sparsity is properly processed.4)Analyze the requirement of the personalized book recommendation system,analyze it from both functional and non-functional aspects,design the system from the participant's point of view and divide the system into user-end modules,server modules and database modules.Finally,the system's implementation process is described in detail.The personalized recommendation online bookstore system developed and designed by the author uses algorithms to perform repeated experiments.It has been able to complete all the basic functions of online bookstores,and at the same time can dynamically and efficiently recommend to users items of interest.