A Machine Learning-Based Book Recommendation System
Anand Kumar Mishra, Arnima Chakravarty, Rajiv Kumar Nath · 2026
The increasing number of digital book libraries on web platforms and libraries require the use of smart, personalized recommender methods that allow users to Navigate large catalogs efficiently. This paper discusses a Machine Learning-based Book Recommendation System that aims to provide personalized recommendations of books. Based on analyzing the preferences, reading habits, and books that are preferred, characteristics of users. The software was developed on Python and powered by frameworks such as Flask for web development, Scikit-learn and TensorFlow for machine learning purposes, and Pandas and NumPy for pre-processing.data. The system is also based on Kaggle-sourced datasets that include users‘ ratings, book information, and user demographic data. It focuses on two key recommendations techniques: collaborative filtering, which compares user preference patterns to give personalised recommendations and popularity-based filtering, which recommends popular or well-rated books based on general user interestThe application of these techniques can provide solutions to problems that are associated with data sparsity and cold-starts, particularly in the case of new users who have little interaction history. The system, run on a local server, is tested on standard performance indicators like precision, accuracy, recall, and F1-score. There are indications that collaborative filtering might provides more contextual and personalized suggestions, a popularity-based filtering does guarantee good recommendations compared to users with less data. The contribution of this paper is related to the following. Evolution of AI-based recommendation systems, and future directions for improvement may involve increased scalability, real-time updatability, and the inclusion of deep learning.