The proposed methods can be extended to accelerate other matrix factorization algorithms beyond FunkSVD by adapting the dynamic pruning approach to suit the specific characteristics of each algorithm. For BiasSVD, which includes user bias, item bias, and overall score of the training data, the pruning process can be modified to consider the significance of these additional factors. By dynamically pruning the less significant biases during both matrix multiplication and latent factor update, the training process can be accelerated without inducing any additional computational resources. Similarly, for SVD++, which includes parameters to reveal implicit feedback and user attribute information, the pruning process can be tailored to account for the significance of these parameters. By dynamically pruning the less significant latent factors related to implicit feedback and user attributes, the training efficiency of SVD++ can be improved. Overall, the key is to analyze the specific structures and characteristics of each matrix factorization algorithm and adapt the dynamic pruning approach accordingly to accelerate the training process.