ResNetMF: Improving Recommendation Accuracy and Speed with Matrix Factorization Enhanced by Residual Networks

Background: Recommendation systems are essential for personalized user experiences but struggle to balance accuracy and efficiency.Objective: This paper presents ResNetMF, an innovative hybrid framework designed to address these limitations by combining the strengths of matrix factorization (MF) and deep residual networks (ResNet). Matrix factorization excels at capturing explicit linear relationships between users and items, while ResNet is employed to model non-linear residuals.Methods: By focusing on refining the baseline MF output through incremental improvements, ResNetMF minimizes redundant computations and significantly enhances recommendation accuracy. The unique architecture of the framework allows it to capture and represent both linear and non-linear relationships between users and items, ensuring robust and scalable performance. Extensive experiments conducted on the widely used MovieLens dataset demonstrate the superiority of ResNetMF over existing methods.Results: Specifically, it achieves a minimum improvement of 7.95% in root mean square error (RMSE) compared to neural collaborative filtering (NCF) and outperforms other state-of-the-art techniques in key metrics such as precision, recall and training efficiency. These results highlight the ability of ResNetMF to deliver highly accurate recommendations while maintaining computational efficiency, making it an efficient approach to real-world application of recommendation systems.Conclusion: By addressing the dual challenges of accuracy and efficiency, ResNetMF offers a balanced and scalable approach to personalized recommendation systems.