Machine Learning-based Book Recommendation Systems: A Comparative Study of CFNN and KNN Algorithms

Abstract

Among recommendation systems, collaborative filtering is a widely used method that leverages user preferences and collective actions to provide accurate book recommendations. With so many books available today, it can be harder and harder for readers to find books that suit their interests. As a result, recommender systems have become a vital tool for addressing this problem head-on, attempting to provide users with personalized book recommendations based on their unique interests and preferences. The studies have employed diverse datasets and machine learning technique KNN with Sparse Matrix, and Deep learning algorithm collaborative filtering Neural Network . Preprocessing carried out by Exploratory Data Analysis. These algorithms have demonstrated a significant improvement in recommendation accuracy. The KNN achieved accuracy levels of 81%, 85%, and 93% for different neighbour values 4, 5, 6 while CFNN achieved the accuracy of 95%. The studies have also delved into understanding the impact of various factors on book recommendations, including user preferences and collaborative patterns among readers and it recommends CFNN is suitable method for recommendation system.