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.
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Larger training datasets continue to improve recommender system performance without observable saturation on typical user-item data.
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Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
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.
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The Unreasonable Effectiveness of Data for Recommender Systems
Larger training datasets continue to improve recommender system performance without observable saturation on typical user-item data.