提出两种新方法，精准筛选推荐系统中影响决策差异的关键特征。
Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender System
- 基于异质处理效应设计无模型筛选方法，不依赖复杂模型
- 在真实推荐系统中提升决策性能，识别出关键影响特征
- 适合动态环境快速筛选特征，对模型错误不敏感
在大规模在线系统中，有效的特征选择对优化上下文多臂老虎机（CMAB）至关重要，次优特征会降低收益、可解释性和效率。传统方法仅关注结果相关性，忽视了不同动作间异质处理效应（HTE）的作用。本文提出两种新型无模型过滤方法：异质增量效应（HIE）和异质分布发散（HDD），专门用于识别驱动HTE的特征。HIE根据特征引发最优动作变化的能力衡量其价值，HDD则评估其对各动作奖励分布差异的影响。这些方法计算高效，对模型误设具有鲁棒性，且适用于多种特征类型，适合在无法频繁重训复杂模型的动态环境中快速筛选。我们在具有已知真实值的合成数据和一个大规模商业推荐系统上验证了HIE与HDD，证明其能持续识别出有影响力的HTE特征，从而提升CMAB性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。