Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems
Modern industrial recommender systems use a deep ranking model to score N candidates against the same user and context features. Standard implementations broadcast context features early in the forward pass, redundantly computing context-only operations N times per request. We present a rank-aware decomposition applicable to the dominant interaction mechanisms in modern recommender architectures-Factorization Machine (FM) pairwise products, Deep Cross Network (DCNv2) cross layers, self-attention, and fully connected (FC) projection layers-built on a single algebraic principle: any linear or bilinear operation over a rank-partitioned input admits an exact block decomposition that moves con...
| Authors | Yevgeny Tkach |
|---|---|
| Primary category | cs.IR |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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