3 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…
cs.LG2025
DimGrow: Memory-Efficient Field-level Embedding Dimension Search
Yihong Huang, Chen Chu
Key feature fields need bigger embedding dimensionality, others need smaller. This demands automated dimension allocation. Existing approaches, such as pruning or Neural Architectu…