A Hybrid Book Recommendation System Using Genetic Algorithm for Enhancing Book Rating

Abstract

Recommendation systems have emerged as the most prevailing systems due to the abrupt use of online services during pandemics from past years. Multi-billiondollar industries such as kindle, Alibaba, amazon, Careem, and many other local online applications of grocery and medicine are heavily dependent on these systems. Applications based on machine learning models increase the accuracy and efficiency of the recommendation system and eliminate the possibility of human effort in finding relevant items. Machine learning models learn, recognize patterns, and make decisions with minimal human intervention based on data. We have developed an innovative and novel book recommendation system. We have used a genetic algorithm to enhance the rating of books and find the distance between similar users and recommend books. The Dataset is being used is taken from Amazon web services and it is available on Kaggle as Books.csv.