Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization

Summary

Meta developed Hierarchical Interest Representation as an upstream representation layer to optimize deep funnel ad ranking across its platforms. The architecture organizes interactions among users, advertisers, and products into a heterogeneous, time-decayed graph and projects sparse raw edges into multi-level super-graphs of latent interest primitives. To encode these structures, a transformer-based hierarchical encoder incorporates multimodal product semantics processed via language models alongside topology-aware attention biases. Computation scales across billions of entities by pairing deep hash ID embeddings with FlexAttention to evaluate graph biases on the fly without materializing full matrices. Self-supervised cross-view distillation trains student representations against broader teacher graph views to yield universal embeddings and interest tokens.

Takeaways

• Hierarchical Interest Representation compresses sparse, dynamic user-ad graphs into multi-tier super-graphs of stable latent interest primitives across varied granularities.
• The system keeps ID memory bounded across tens of billions of entities using deep hash embeddings and utilizes FlexAttention to compute pairwise graph-structural biases on the fly.
• Multimodal advertiser and catalog data processed through vision and language models are fused directly into graph nodes, enabling semantic generalization to rare or unseen entities.

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