Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling
Making friend suggestions work at massive scale without exploding in memory
Researchers built a friend-recommendation system that works on social networks with hundreds of millions of users by solving two critical engineering problems: compressing user identity data by 98 percent and speeding up searches through friend lists by 100-fold. In real-world tests at production scale, the system increased friend additions from recommendations by 16 percent.
Social platforms need to recommend friends to hundreds of millions of people simultaneously, but existing approaches either waste enormous amounts of memory or become too slow to serve recommendations in real time. This work makes both problems solvable without tradeoffs — the same system that fits in memory also responds fast enough for live use. Platforms can now deploy more sophisticated recommendation logic without building expensive new infrastructure.