A rank-aware block decomposition for linear and bilinear operations in recommender models (FM, DCNv2, attention, FC) reduces redundant context feature computation to once per request with identity-equivalent results, plus rDCN variant for deeper layers.
Dcn v2: Improved deep & cross network and practical lessons for web-scale learning to rank systems
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TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
HSTU-based generative recommenders with 1.5 trillion parameters scale as a power law with compute up to GPT-3 scale, outperform baselines by up to 65.8% NDCG, run 5-15x faster than FlashAttention2 on long sequences, and improve online A/B metrics by 12.4%.
RankElastor mitigates embedding collapse via spectrum-robust token mixing and GLU-based P-FFNs, yielding better performance and scaling on industrial recommendation datasets.
DUET pre-trains dedicated transformers for click and conversion streams, yielding up to 0.38% NE reduction over baselines in OCVR prediction.
IEFF enables retrain-free feature efficiency rollouts in ranking systems by elastically controlling feature coverage at serving time, achieving 5x faster rollouts, zero retraining GPU cost, and 50-55% less performance degradation than abrupt feature removal.
This paper defines six dimensions of notification message quality, surveys LLM improvements over templates with reported CTR gains of 8-14.5%, and introduces a decision framework for when LLM generation is the binding constraint.
A budget split intervention reduces gender skew in online ad delivery by incorporating users with unknown demographics alongside targeted inferred-gender groups.
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Into the Unknown: Accounting for Missing Demographic Data when Mitigating Ad Delivery Skew
A budget split intervention reduces gender skew in online ad delivery by incorporating users with unknown demographics alongside targeted inferred-gender groups.