Memory-Efficient Graph Filtering for Scalable Collaborative Filtering

Mem-GF introduces a memory-efficient graph filtering method that approximates polynomial graph filters using Krylov subspaces, avoiding storage of the full item similarity graph. It achieves up to 5.74× lower memory usage and 4.38× faster runtime while outperforming state-of-the-art methods in accuracy and scaling to datasets with tens of millions of interactions.