Book Recommendation System Using Hybrid Filtering
Cynthia Jayapal, S. Gokul, Harshavardhan S.V.
Abstract
In the modern world, where individuals may enter a library or browse online platforms without a specific book in mind. However, every reader has their own unique interests and preferences. With the help of Book Recommendation System we can offer a solution by utilizing algorithms to suggest books based on a reader’s interests. This type of system is commonly employed by online eBook providers, such as Google Play Books. With the aim of reducing the need to search for books and providing personalized recommendations, we propose the development of a website for students, designed to simplify the book selection process and eliminate confusion. By leveraging a user’s previous checkout history and search data, the website can recommend books based on the user’s interests. There are several models available for building such a system, including Content-based recommendation, collaborative filter-based recommendation, and popularity-based recommendation. In this paper, we propose to use a hybrid-based recommendation system, combining content-based filtering and shared filtering. approaches for optimal results.