Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization

What this post added

This post introduces Hierarchical Interest Representation, a new upstream representation layer for Meta Ads deep funnel optimization. It details the research area, its technical innovations (transformer-based graph learning, bias-aware attention, self-supervised cross-view distillation), and its application in connecting users' inferred interests with advertiser offerings. The post highlights the system's ability to blend real-world knowledge with engagement signals, process multimodal content via LLMs, and generate universal embeddings and interest tokens. It also outlines the technical challenges of user inferred signal dynamics, large networks with sparse connections, and long-range global relationships, and describes the architecture including the enriched engagement graph and hierarchical encoder with bias composition and attention kernel.