Analyzing Algorithms: A Comparative Study of Book Recommendation Systems
L. Eswarsairam, K. Varun Kumar, Prajay Reddy, Kantipudi Sai Sri Rohith, Anjali, Amit Abhishek · 2024
In the era of digital libraries and online bookstores, efficient recommendation systems are crucial for helping users discover relevant books. This research investigates the performance of various machine learning algorithms, including Decision Trees, Random Forests, K-Nearest Neighbors, Linear Regression, Ridge Regression, RANSAC Regression, Gradient Boosting, LightGBM, and XGBoost, in predicting user preferences. The study evaluates the performance of these algorithms using metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R -squared. By analyzing the strengths and weaknesses of each approach, the research provides insights into the most suitable algorithms for different recommendation scenarios.The findings of this research can guide developers and researchers in designing and implementing effective book recommendation systems. By understanding the limitations and potential of various machine learning techniques, it is possible to create personalized and engaging reading experiences for users.