Machine Learning based Efficient Recommendation System for Book Selection using User based Collaborative Filtering Algorithm
Madhuri Kommineni, P. Alekhya, Tiramdasu Vyshnavi, V. Aparna, K. Swetha, V. Mounika
Abstract
Recommender system is a new generation of internet tool that helps users to access the web and receive information about their preferences. Using an online recommender is comparatively an easy and faster procedure to purchase items and this is done quickly. Recommendation systems plays an indispensable role in ecommerce websites to help users in identifying the right goods. One of the best methods to increase profits and attract customers is a recommendation process. The existing methodologies allow the systems to collect the irrelevant data and lead to a downfall in attracting the users and completing their work in a quick and reliable way. This paper provides an overview of the Recommendation Systems that is currently employed in the operations of the online book shopping domain. This paper proposes a simple understandable system for book recommendations that help readers to suggest the right book, which is to be studied next. In recent years, information analysis challenge has been focused on for the administration recommendation system. For clients, network assets are completely linked and quickly developed. The proposed method works on training, feedback, management, reporting, configuration, and using it to offer useful information to the user in order to aid in decision-making and data item recommendations. We have used a User Based Collaborative Filtering (UBCF) approach and measured the performance of similarity measures in recommending books to a user. The proposed system’s overall architecture is introduced and its implementation is represented with a model design.