GenRecs Tokenization: Transforming Ads Recommendation at Meta with Stable Model Vocabulary

• Generative Recommenders (GenRecs) represent a paradigm shift from scoring every ad to reasoning about ad content and user preferences together in order to predict the best outcome for advertisers. Leveraging the world knowledge built into LLMs makes ad matching more intelligent and precise, compounding gains for advertisers.
• GenRecs Tokenization is the foundational data representation that makes this possible. Rather than maintaining a dedicated ID for every item, GenRecs Tokenization represents each item with a set of tokens from a stable semantic vocabulary.
• Paired with Meta’s Generative Ads Model (GEM) and Meta’s Adaptive Ranking Model, GenRecs Tokenization has delivered a +0.35% lift in conversions across surfaces for advertisers.

GenRecs improves the way Meta’s ads recommendation systems make predictions today. Short for Generative Recommenders, GenRecs shifts our approach from scoring every individual ad candidate to leveraging LLM architecture to deeply reason about ad content and user preferences simultaneously. By pairing pre-trained world knowledge with a semantic understanding of what an ad actually represents, the system moves beyond click-pattern matching to understand underlying user preferences. This allows the model to predict the most relevant ad for each person, even when surfacing brand-new products with no past product, ad or company engagement history. 

GenRecs Tokenization is the foundational data representation that makes this possible. Instead of maintaining a dedicated ID for every concept in the world, language models compose a small vocabulary of semantic tokens to represent any concept. Because tokens describe what an item (e.g., ads, products, services, etc.) is, the model can relate an item it has never seen engagement for, to items it knows well. A newly launched “trail shoe” inherits what the model has already learned about that category, rather than starting a new representation with no signal.

GenRecs Tokenization introduces a structured vocabulary approach for ad recommendations, enabling GenRecs for both retrieval and ranking across every item type in the system, including ads, products, pages, and pixels.