BookFinder: A Machine Learning Based Recommendation System for Book Lovers

This paper presents BookFinder, a machine learning–based book recommendation system designed to assist readers in discovering books that closely match their interests. The system uses a similarity–based approach, where book metadata such as title, author, language, publisher, and rating are analyzed to identify patterns and generate relevant recommendations. The KNearest Neighbors (KNN) algorithm with cosine similarity is used to compute closeness between books in the feature space. The frontend is built with React.js to provide an interactive and responsive user interface, while Node.js and Express.js serve as the intermediary layer that communicates with the backend machine learning API developed in Python using Flask. The model was trained using a dataset containing 11,124 book records sourced from Kaggle. Features such as real-time search, filtering options, and search suggestions enhance usability and allow users to easily discover relevant books. The system successfully returns book recommendations based on user input without requiring historical user-rating data or login details. This work demonstrates that KNN-based collaborative filtering can be applied effectively to generate meaningful book recommendations using metadata alone, making BookFinder a scalable and lightweight solution for book discovery platforms.