Personalized Book Recommendation System using TF-IDF and KNN Hybrid

Abstract

A recommendation system helps an organization to create loyal customers and build trust by offering their desired products and services. These systems today are so powerful that they can handle the new customer too who has visited the site for the first time. With the increasing number of books, people prefer to use e-books. Today, online businesses have emerged that are dedicated only for e-books. They allow their users to purchase any books of their interest or even read them online. This improves their business targets. To make their users engaged, they use machine learning models that recommend users the books based on their preferences. Such a system is called Book Recommendation System. Over the past, a large number of book recommendation systems have been built, most of them are found to be useful for both the organization and the users, and are being put into use in the real world. In this proposed system, we build a Book Recommendation System which recommends a set of books to users based on their previous ratings and readings using content-based filtering, collaborative filtering, and hybrid filtering model. This will save users time in searching the books of their interest.