An Effective Book Recommendation System using Weighted Alternating Least Square (WALS) Approach

Abstract

Book recommendation systems are essential resources for connecting people with the correct books, encouraging a love of reading, and sustaining a vibrant literary ecosystem in an era when information overload is a prevalent problem. With the emergence of digital libraries and large online book retailers, readers may no longer find their next great literary journey without the help of customized book suggestions. This work offers a novel way to improve book recommendation systems using the Weighted Alternating Least Squares (WALS) technique, which is intended to uncover meaningful patterns in user ratings. The suggested approach minimizes the Root Mean Square Error (RMSE), a crucial indicator of recommendation system (RS) performance, in order to tackle the problem of optimizing recommendations. By representing user-item interactions as a matrix factorization problem, the WALS approach improves the recommendation process. In contrast to conventional techniques, WALS adds weighted elements that highlight specific user-item pairings' significance, increasing the recommendations' accuracy. Through an empirical study, the proposed approach demonstrates a significant reduction in RMSE when compared to standard RS, highlighting its effectiveness in enhancing the quality of book recommendations. By leveraging weighted matrix factorization, the proposed method adapts to the nuanced preferences and behaviors of users, resulting in more accurate and personalized book recommendations. This advancement in recommendation technology is poised to benefit both readers and the book industry by fostering more engaging and satisfying reading experiences.