LeAP is a model-agnostic plug-in that learns to permute and rank heterogeneous sparse features for selection, achieving SOTA on public datasets and removing over 3600 redundant features in a production system with 12k features.
IEEE Transactions on Knowledge and Data Engineering36(9) (2024)
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LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
LeAP is a model-agnostic plug-in that learns to permute and rank heterogeneous sparse features for selection, achieving SOTA on public datasets and removing over 3600 redundant features in a production system with 12k features.