arXiv cs.LGPaper
Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling
This is a legitimate systems paper with a real number: cutting a 200GB embedding table by 98% while preserving ranking quality is directly reusable for any team running large-scale recommendation GNNs. Practical infra engineering rather than a new idea, useful for ML infra teams at social or marketplace platforms dealing with high-cardinality ID features.