Abstract: The development and implementation of a content-based book recommendation system using machine learning algorithms. To improve the quality and accuracy of the recommendations given, the system employs a hybrid method that combines content-based filtering and collaborative filtering techniques. Content-based filtering is employed to recommend items based on their similarity to previously liked items. By analyzing the characteristics and features of books, the system identifies similarities between them and recommends books that share similar attributes. Collaborative filtering is utilized to leverage user ratings and establish correlations between users and items. This allows the system to recommend books that are popular among users with similar preferences. The implementation of the recommendation system is built upon the Django framework, which provides a robust and scalable web development environment. The system incorporates various machine learning algorithms, including feature extraction, similarity measures, and recommendation models, implemented by using the Python programming language and Jupyter Notebook for exploratory data analysis. Overall, this project shows the successful development and implementation of a content-based book recommendation system.

| DOI: 10.17148/IARJSET.2023.10852