Meta turns to generative AI to revamp its recommendation feeds
Meta researchers just detailed how generative retrieval can predict user intent instead of relying on traditional database searches. By introducing frameworks called LIGER and Mender, the system treats recommendations as sequence predictions, slashing storage costs while using LLMs to capture subtle user preferences. Why it matters: It shows generative models could soon replace the classic embedding systems powering the internet's biggest feeds.