# Preference-based recommendation

Meta introduced Your algorithm, which lets Facebook users state in natural language what they want and do not want to see, with those preferences applied to both Feed and Reels. Previously the recommendation systems learned mainly from implicit engagement signals, which scale well but adapt slowly and capture little nuance. The system uses a collection of large language models to infer interests, validate preference changes and perform a final rerank, while a supplementary fetch and cache invalidation make changes take effect faster; Meta notes that interpreting ambiguous input and responding more precisely remain open problems.

Meta introduced Your algorithm, which lets Facebook users state in natural language what they want and do not want to see, with those preferences applied to both Feed and Reels. Previously the recommendation systems learned mainly from implicit engagement signals, which scale well but adapt slowly and capture little nuance. The system uses a collection of large language models to infer interests, validate preference changes and perform a final rerank, while a supplementary fetch and cache invalidation make changes take effect faster; Meta notes that interpreting ambiguous input and responding more precisely remain open problems.