# Ads Recommendation

Meta has released the next generation of Andromeda, its personalized ads retrieval system, designed jointly across model, system and GPU hardware. Retrieval previously could not afford ranking-stage model depth at its scale; the new architecture brings large-language-model-style mixture-of-experts into retrieval, approaching ranking-level performance at roughly 300x fewer inference FLOPs than the 2024 retrieval model. The combined single-GPU pipeline processes over one billion ads per second on one server, which Meta calls a new throughput benchmark for retrieval-stage models, and Meta reports a 2.4% conversion increase on Facebook since 2025.

Meta has released the next generation of Andromeda, its personalized ads retrieval system, designed jointly across model, system and GPU hardware. Retrieval previously could not afford ranking-stage model depth at its scale; the new architecture brings large-language-model-style mixture-of-experts into retrieval, approaching ranking-level performance at roughly 300x fewer inference FLOPs than the 2024 retrieval model. The combined single-GPU pipeline processes over one billion ads per second on one server, which Meta calls a new throughput benchmark for retrieval-stage models, and Meta reports a 2.4% conversion increase on Facebook since 2025.

Retrieval quality in dynamic product advertising could previously be compared only through sparse, biased engagement logs or costly online experiments. The new method compresses a user's engagement history into a small set of intent representatives grouped by semantic ID and stratified by event type, then has a large language model judge each candidate product against every intent and aggregate the results into a user-level score. On 30 days of production traffic, the score ranked six retrieval sources consistently with conversion precision and recall, and moved in the same direction as online CTR and CVR across four A/B tests.

Retrieval quality in dynamic product advertising could previously be compared only through sparse, biased engagement logs or costly online experiments. The new method compresses a user's engagement history into a small set of intent representatives grouped by semantic ID and stratified by event type, then has a large language model judge each candidate product against every intent and aggregate the results into a user-level score. On 30 days of production traffic, the score ranked six retrieval sources consistently with conversion precision and recall, and moved in the same direction as online CTR and CVR across four A/B tests.

Meta has replaced one dedicated ID per item in its ads recommendation system with a short sequence of tokens drawn from a stable semantic vocabulary, so the model can infer relevance for new ads that have no engagement history. Paired with Meta's Generative Ads Model and Adaptive Ranking Model, the representation delivered a 0.35% lift in conversions and is used for both retrieval and ranking.

Meta has replaced one dedicated ID per item in its ads recommendation system with a short sequence of tokens drawn from a stable semantic vocabulary, so the model can infer relevance for new ads that have no engagement history. Paired with Meta's Generative Ads Model and Adaptive Ranking Model, the representation delivered a 0.35% lift in conversions and is used for both retrieval and ranking.