Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization

Introduced Hierarchical Interest Representation, an upstream representation layer for Meta Ads deep funnel optimization. This system uses transformer-based graph learning with bias-aware attention and self-supervised cross-view distillation to learn multi-hierarchical interest representations across a large graph. It fuses real-world knowledge (multimodal advertiser and product content processed through LLMs) with engagement signals to enrich sparse interactions and generalize to rare entities. The architecture includes an enriched engagement graph (typed, weighted, time-decayed) and a hierarchical encoder with structure encoders (node, position, edge) and bias composition for topology-aware attention kernels. The system aims to reduce dimension, enrich knowledge, provide unified relational representation, and leverage multi-hierarchical granularities.