Personalized AI Recommendations

Builds a per-customer “taste vector” from real behavior — presentation likes and dislikes, and purchase history — living in the same space as product embeddings, so a nearest-neighbor lookup returns genuinely personal recommendations. Purchases outweigh likes, a thumbs-down pushes taste away, and recent signals dominate as older ones decay, all recomputed quietly as customers interact.