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. Hierarchical Interest Representation is an upstream representation layer designed to improve upon Meta’s deep funnel ranking optimization. It aims to connect businesses with the population of people on our platforms who carry the most genuine, latent interest in what they offer. The system is intended to function across Meta’s broader recommendation…