How LAST Enables Real‑Time Learning for Re‑Ranking in E‑Commerce Recommendations
This article presents LAST, a novel Learning-at-Serving-Time approach that enables real‑time online learning for re‑ranking in industrial recommendation pipelines, eliminating feedback latency, detailing its architecture, challenges, experimental validation, and practical advantages over traditional online learning methods.
