ShuffleGate: A Unified Gating Mechanism for Feature Selection, Model Compression, and Importance Estimation

Feature selection, dimension selection, and embedding compression are fundamental techniques for improving efficiency and generalization in deep recommender systems. Although conceptually related, these problems are typically studied in isolation, each requiring specialized solutions. In this paper, we propose ShuffleGate, a unified and interpretable mechanism that estimates the importance of feature components, such as feature fields and embedding dimensions, by measuring their sensitivity to value substitution. Specifically, we randomly shuffle each component across the batch and learn a gating value that reflects how sensitive the model is to its information loss caused by random replacement. For example, if a field can be replaced without hurting performance, its gate converges to a low value--indicating redundancy. This provides an interpretable importance score with clear semantic meaning, rather than just a relative rank. Unlike conventional gating methods that produce ambiguous continuous scores, ShuffleGate produces polarized distributions, making thresholding straightforward and reliable. 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.