A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
Interformer: Towards effective heterogeneous interaction learning for click-through rate prediction
3 Pith papers cite this work. Polarity classification is still indexing.
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Speculative precomputation of foundation-model user–item embeddings decouples heavy inference from the serving path and yields 0.67% revenue gain at Meta ads scale.
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.
citing papers explorer
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Efficient Retrieval Scaling with Hierarchical Indexing for Large Scale Recommendation
A jointly learned hierarchical index with cross-attention and residual quantization scales exact retrieval in foundational recommendation models, deployed at Meta with additional performance from test-time training on index nodes.
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SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling
Speculative precomputation of foundation-model user–item embeddings decouples heavy inference from the serving path and yields 0.67% revenue gain at Meta ads scale.
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LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
LoKA claims to make FP8 practical for large recommendation models via statistical probing, model adaptations, and accuracy-aware kernel dispatch.