2024/09/20 by Zhenyu Zhao, Zhao, Zhenyu, Yexi Jiang +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2409.13888
openalex publication_date 2024/09/20 · openalex created_date 2024/10/25 · openalex updated_date 2026/07/28
Effective feature selection is essential for optimizing contextual multi-armed bandits (CMABs) in large-scale online systems, where suboptimal features can degrade rewards, interpretability, and efficiency. Traditional feature selection often prioritizes outcome correlation, neglecting the crucial role of heterogeneous treatment effects (HTE) across arms in CMAB decision-making. This paper introduces two novel, model-free filter methods, Heterogeneous Incremental Effect (HIE) and Heterogeneous Distribution Divergence (HDD), specifically designed to identify features driving HTE. HIE quantifies a feature's value based on its ability to induce changes in the optimal arm, while HDD measures its impact on reward distribution divergence across arms. These methods are computationally efficient, robust to model mis-specification, and adaptable to various feature types, making them suitable for rapid screening in dynamic environments where retraining complex models is infeasible. We validate HIE and HDD on synthetic data with known ground truth and in a large-scale commercial recommender system, demonstrating their consistent ability to identify influential HTE features and thereby enhance CMAB performance.