Utilizing the Artificial Bee Algorithm to Enhance the Accuracy of Book Recommendation Systems: A Case Study on Goodreads dataset

Abstract

 With the significant increase in the number of books available online, especially following the COVID-19 pandemic, it has become essential to develop more accurate and efficient recommendation systems to help readers find books that match their preferences. This research aims to enhance the accuracy of book recommendation systems using the Artificial Bee Colony (ABC) algorithm, leveraging data from the Goodreads platform. Book recommendation systems are crucial for providing personalized suggestions to users based on their preferences and behaviors. However, these systems face recommendation accuracy challenges due to data sparsity, scalability, and changing user preferences. In this study, we demonstrate the application of the Bee Algorithm to improve recommendation accuracy, with a thorough evaluation using various metrics such as precision, recall, and mean squared error (MSE). The results indicate that the Bee Algorithm can achieve significant improvements in recommendation accuracy, thereby offering more accurate suggestions and better meeting user needs.