A book recommendation system based on springboot and collaborative filtering
Nowadays, in the world of information overload, it becomes more and more difficult for people to obtain the data they need. With the help of recommendation technology, it is much easier to filter the required information from a large amount of data. Therefore, it is of great practical significance to use recommendation algorithms in systems such as e-commerce, books, movies, music and so on. In this context, recommendation system is widely used on the Internet. Collaborative filtering algorithm is the most popular recommendation algorithm, and its application and research are of great significance.
The book recommendation system mainly uses collaborative filtering algorithm to realize book recommendation on the basis of book borrowing and other basic functions. The algorithm first establishes the borrower's book score matrix, then determines the borrower groups with adjacent interests through cluster analysis, then weights and de weights the books loved by the target users according to their favorite books, and finally sorts the books with high scores to recommend them to the borrower.
The book recommendation system based on collaborative filtering uses the collaborative filtering algorithm in Mahout framework as the core recommendation algorithm, uses Java Web technology to build a book borrowing platform, uses SpringBoot and MyBatis frameworks at the back end, and LayUI and Echarts frameworks at the front end to realize the data interaction between the foreground, background and database. The background system is a management system for administrators to manage and maintain all kinds of data in the library, mainly including: the management of borrowers, books, labels, borrowing information, administrator information and other data; The foreground system provides users with the functions of selecting books, viewing book information, recommending, collecting, borrowing, returning books, viewing borrowing records and so on.