Rank-Aware Decomposition Technique Reduces Computation in Recommender Systems by 87.5%
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[Submitted on 24 May 2026]
Toasted to a respectable shade. No regrets, no crumbs left.
Summary
This paper presents a rank-aware decomposition technique for deep ranking models in industrial recommender systems. The key insight is that standard implementations redundantly compute context-only operations N times per request when scoring N candidates. The authors propose an algebraic decomposition that moves context-only computation from once-per-candidate to once-per-request, applicable to FM pairwise products, DCNv2 cross layers, self-attention, and FC projection layers. Applied to a production DLRM-style ranker, this increases per-pod throughput by 87.5% (47% reduction in peak pod count) with identical model predictions. The paper also introduces rDCN, an architectural variant of DCNv2 that maintains rank discipline across depth, matching accuracy at 67% fewer total FLOPs.
Key quotes
· 5 pulledWe present a rank-aware decomposition applicable to the dominant interaction mechanisms in modern recommender architectures
Applied to a production DLRM-style ranker without any architectural change, the decomposition increases per-pod throughput by 87.5% (a 47% reduction in peak pod count) at identical model predictions.
The identity-equivalent decomposition applies only at the first layer of cross networks and self-attention, since each layer mixes ranks in its output.
We further introduce rDCN, an architectural variant of DCNv2 that maintains rank discipline across depth and matches DCNv2 accuracy within training noise at 67% fewer total FLOPs.
any linear or bilinear operation over a rank-partitioned input admits an exact block decomposition that moves context-only computation from once-per-candidate to once-per-request
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