Adaptive recommendation systems: A comparative analysis of KNN-based algorithms and hybrid models
Jianing Yu, Anbang Wang · Applied and Computational Engineering · 2024
This study presents a comprehensive comparative analysis of various recommendation algorithms, focusing on their efficacy in predicting user preferences. The algorithms examined include KNNBaseline, KNNWithMeans, KNNBasic, KNNWithZScore, and Singular Value Decomposition (SVD), each representing distinct methodologies within the collaborative filtering paradigm. Performance was evaluated using two error metrics: Root Mean Square Error (RMSE) and Mean Absolute Error (MAE), reflecting both the magnitude and absolute values of prediction errors. The results reveal subtle differences in performance across the algorithms, with no single method demonstrating marked superiority. These findings underscore the importance of understanding the nuanced behavior of different algorithms and their suitability for specific applications or contexts. The study contributes valuable insights to the field of recommendation systems, enhancing the understanding of algorithmic behavior, and offers guidance for practitioners in selecting and optimizing algorithms to meet specific needs and objectives. Future research directions include the exploration of additional algorithms, diverse datasets, and alternative evaluation metrics.