At 10×: the candidate generation index grows 10×. ANN index quantisation (reducing embedding precision from 32-bit float to 8-bit integer) reduces memory by 4×. The scoring model may be distilled — a smaller student model trained to approximate a larger teacher model, matching 95% of the quality at 20% of the compute.
At 100×: real-time personalisation with bandits — reinforcement learning that explores the recommendation space while exploiting known user preferences. The feedback loop runs in near-real-time (seconds, not hours), enabling rapid adaptation to user mood and context.