2 papers
cs.LG2026
LeAP: Learnable Adaptive Permutation for Feature Selection in Heterogeneous and Sparse Recommender Systems
Yihong Huang, Chen Chu, Fei Chen +3
Modern industrial recommender systems rely on thousands of heterogeneous features -- ranging from low-dimensional scalars (e.g., statistical value) to high-dimensional embeddings (…
cs.LG2026
ShuffleGate: Scalable Feature Optimization for Recommender Systems via Batch-wise Sensitivity Learning
Yihong Huang, Chen Chu, Fan Zhang +4
Feature optimization -- specifically Feature Selection (FS) and Dimension Selection (DS) -- is critical for the efficiency and generalization of large-scale recommender systems. Wh…