pith:VYSY4OO7
ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning
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
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.
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.
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
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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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