Design And Implementation Of Personalized Book Recommendation System Based On Hybrid Recommendation

With the rapid development of science and technology,the amount of information in the network is showing a rapid growth trend,leading to the phenomenon of information overload.So,it is difficult for people to quickly and efficiently find the information they need and are interested in from the vast amount of information.To address this challenge,personalized recommendation systems have emerged in the public eye and are widely used in multiple fields.Similarly,in the field of books,it is difficult for readers and users to select books that meet their needs when faced with a large and diverse variety of book resources.So introducing a recommendation system to help users select suitable book resources for their needs and improve their reading experience.However,traditional and single recommendation algorithms may encounter cold start issues,resulting in poor recommendation performance.In addition,popular items on the platform can affect recommendation results,resulting in recommended items that do not meet the personalized needs of users.This topic aims to provide users with more accurate personalized book recommendation services,design and implement a personalized book recommendation system based on hybrid recommendation,and optimize the collaborative filtering algorithm.The main research content is as follows:User similarity improvement based on collaborative filtering.When the traditional collaborative filtering algorithm recommends users,some popular books will affect the calculation of the similarity between users and books,so it will be interfered with,which is not conducive to the recommendation effect of the system.This topic decides to incorporate penalty factor to reduce the influence of popular books on similarity calculation,optimize the cosine similarity calculation method,and improve the accuracy of recommendation.Implement hybrid recommendation algorithm.The traditional single recommendation algorithm has some defects,which may lead to cold start and poor recommendation effect in the book recommendation system.Therefore,this topic adopts the mixed recommendation strategy to realize the personalized book recommendation system,combining the collaborative filtering algorithm with the content-based algorithm,making the two algorithms complementary,giving full play to the advantages of their own algorithms,solving the traditional recommendation system cold start and other problems,so that the system recommendation results more accurate.Design and implementation of a book recommendation system.This system is developed using the Python programming language on the Pycharm platform.The frontend design adopts the Vue framework and the back-end uses the Django framework to achieve system development by separating the front-end and back-end.This project is committed to meeting the personalized needs of users and recommending books to users through better system pages.