Meta· Llama / infrastructure

Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization

Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users’ inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are an in-house transformer based graph learning with bias-aware attention and self-supervised cross-view distillation, learning multi-hierarchical interest representations across a large graph. Hierarchical Interest Representation blends real-world knowledge with engagement signals – multimodal advertiser and product content processed through LLMs enriches sparse interactions, enabling generalization to rare and unseen entities. Hierarchical Interest Representation outputs universal embeddings for ads entities and Bag-of-Meaning interest tokens that have the potential to power new personalization, retrieval, supervision, and specialized ranking architectures across the ads stack. Trained end-to-end on real Meta ads data at the scale of billions of interactions. …

Announced

15 Jul 2026 17:00 UTC