The proposed techniques can be integrated with other orthogonal approaches, such as parallelization or hardware acceleration, to further improve the training efficiency of large-scale recommendation systems. By combining dynamic pruning with parallel computing techniques, the computational workload can be distributed across multiple processors or nodes, allowing for faster processing of matrix factorization tasks. Additionally, integrating hardware acceleration, such as utilizing GPUs or specialized AI chips, can further enhance the computational speed of the training process. The dynamic pruning approach can be implemented in conjunction with these techniques to optimize the utilization of computational resources and maximize the efficiency of training large-scale recommendation systems. By leveraging a combination of dynamic pruning, parallelization, and hardware acceleration, the overall performance and scalability of recommendation systems can be significantly enhanced.