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pith:2025:VYSY4OO7JRXIH3HT3SWUEIEYPM
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ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning

Chen Chu, Fan Zhang, Liping Wang Fei Chen, Ruiduan Li, Yihong Huang, Yu Lin, Zhihao Li

ShuffleGate estimates importance of feature components by training gates on sensitivity to their random shuffling across batches, unifying feature selection, dimension selection, and embedding compression.

arxiv:2503.09315 v6 · 2025-03-12 · cs.LG

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Claims

C1strongest claim

Our gating module can be seamlessly applied at the feature field, dimension, or embedding-entry level, enabling a unified solution to feature selection, dimension selection, and embedding compression. Experiments on four public recommendation benchmarks show that ShuffleGate achieves state-of-the-art results on all three tasks.

C2weakest assumption

That randomly shuffling a component across the batch produces an information-loss signal whose magnitude is a faithful and unbiased measure of that component's true importance to the downstream task, without the shuffling process itself introducing artifacts that the gate then learns to exploit.

C3one line summary

ShuffleGate learns polarized importance gates by measuring model sensitivity to random component shuffling, unifying feature selection, dimension selection, and embedding compression with SOTA results on four recommendation benchmarks.

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First computed 2026-06-01T02:03:19.982440Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

ae258e39df4c6e83ecf3dcad4220987b14412ac216701443da361e69da2728e3

Aliases

arxiv: 2503.09315 · arxiv_version: 2503.09315v6 · doi: 10.48550/arxiv.2503.09315 · pith_short_12: VYSY4OO7JRXI · pith_short_16: VYSY4OO7JRXIH3HT · pith_short_8: VYSY4OO7
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/VYSY4OO7JRXIH3HT3SWUEIEYPM \
  | jq -c '.canonical_record' \
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Canonical record JSON
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