Instacart’s semantic IDs bridge unrelated products for better recommendations
Instacart replaced its rigid taxonomy with learned semantic IDs—a compressed embedding that groups products by meaning rather than category. This lets cold‑start items surface alongside related goods across unrelated branches, improving discovery and recommendation coverage at a catalog of millions. The approach combines contrastive training and vector quantization for scalable, discoverable product representations.