Online Book Recommendation System using Custom Recommender

Abstract

Recommendation systems can be considered as one of the most popular tools to raise the profit and retain users. In most of the fields Recommender systems are being used. The aim of the recommender system is that it suggests content for users based on their previous choices or what type of taste they are having. When there is a need for implementing an effective recommender system, it should always be diverse in content and it is not supposed to be biased towards the most popular content. In this perspective, the content-based filtering will provide well-suited results for the user. This research study attempts to propose a Recommender system for suggesting consecutive appropriate books for the user to read. The proposed recommender system is designed by using item-based collaborative filtering, content-based collaborative filtering (using Title, Author, Publisher, Category as features), Content-Based Collaborative Filtering (using Summary as a feature), Custom Recommender and at the end different recommenders are compared.