Personalized Online Book Recommendation System Using Hybrid Machine Learning Techniques - Rajalakshmi, S.; Indumathi, G.; Elias, Arun; Priya, G. Shanmuga; Muthulakshmi. R, Vidhya (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Recently, the scientific world has become interested in recommender systems research due to its exponential development. The COVID-19 pandemic has caused an exponential increase in the number of books available online, making it extremely difficult for readers to identify relevant books within the large e-book sector. According to user ratings and interests, personal recommendation systems have developed as an efficient way to search for relevant books. Recommendation systems are robust emerging technologies that aid consumers in finding products that they wish to purchase. Recommendation systems are often implemented to suggest proper goods to end customers. Recently, websites that offer books online contest with one another based on a wide range of criteria. One of the best methods to boost profits and keep customers is a recommendation system. Users are not satisfied with the current systems since they extract unnecessary information from them. To generate highly effective and productive recommendations, this study proposes the Personalized Online Book Recommendation System (PO-BRS), which is based on machine learning techniques. The authors proposed hybrid machine learning approaches that combine two or more algorithms to improve the recommendation system's ability to suggest books based on the interests of the reader. As a result, recommendations based on a specific book are found to be more accurate and profitable than systems that depend on user input.