个性化图书推荐系统设计与实现：基于协同过滤的技术应用

In today's world where talents are scarce and knowledge is crucial, the demand for book information has increased. With the abundance of books available, readers find it increasingly difficult to discover books of interest in a short amount of time. To address this issue, professionals in the field of computer science have proposed and implemented a personalized book recommendation system using data mining techniques. This system analyzes the historical behavior of library users to recommend books tailored to individual preferences. This paper focuses on the design and implementation of a book recommendation system based on collaborative filtering among many recommendation algorithms. It provides a comprehensive overview of the concept of collaborative filtering, the steps involved in algorithm implementation, and the technical requirements for system development. The key words in this document include books, recommendation systems, and collaborative filtering. This research aims to explore how collaborative filtering can be applied to the design and implementation of a book recommendation system to assist readers in finding books that align with their interests. Through the utilization of data mining techniques and analysis of user behavior, this system offers a user-friendly solution to the challenge of discovering relevant books in a vast collection.