Accelerating Matrix Factorization Training for Faster Recommendation Systems

The authors propose algorithmic methods to accelerate matrix factorization (MF) training for recommendation systems, without requiring additional computational resources. They observe fine-grained structured sparsity in the decomposed feature matrices and leverage this to dynamically prune insignificant latent factors during matrix multiplication and latent factor update, leading to significant speedups.