Machine Learning-Based Book and Library Recommendation Application Using Content-Based Filtering Method

Abstract

This research focuses on the development of a Book Recommendation System application, which aims to simplify book searches based on reader preferences. Faced with the challenge of selecting books that match their interests, readers often find it difficult to navigate the vast array of information available. The availability of different genres and authors can make the selection process complicated and time-consuming. Therefore, the development of a Book Recommendation System application is a relevant and necessary solution. The author's research question is how to optimize the book search experience through accurate recommendation algorithms. This project will use a collaborative algorithm-based recommendation model to analyze reader behavior patterns and provide accurate book recommendations based on the similarity of reader preferences. The application that the author designed allows readers to easily find suitable books. The author is committed to overcoming readers' difficulties in dealing with the abundance of information, increasing the accessibility of books, and improving reader satisfaction. The success of the project is measured by the application's ability to provide satisfactory recommendations, find relevant works, and simplify the literature exploration process. With the development of the era of vast information, the development of a Book Recommendation System is a relevant solution to guarantee a more enjoyable and efficient reading experience for readers