Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Traditional methods rely on agency models, such as decision trees or neural networks, to estimate feature importance. However, this approach is inherently limited, as the agency models may fail to learn effectively in all scenarios due to suboptimal training conditions (e.g., feature collinearity, high-dimensional sparsity, and data insufficiency). In this paper, we propose AltFS, an Agency-light Feature Selection method for deep recommender systems. AltFS integrates semantic reasoning from Large Language Models (LLMs) with task-specific learning from agency models. Initially, LLMs will generate a semantic ranking of feature importance, which is then refined by an agency model, combining world knowledge with task-specific insights. Extensive experiments on three public datasets from real-world recommender platforms demonstrate the effectiveness of AltFS. Our code is publicly available for reproducibility.

翻译：特征选择在推荐系统中对于提升模型效率与预测性能至关重要。传统方法依赖决策树或神经网络等代理模型来估计特征重要性。然而，该方法存在固有局限，由于次优的训练条件（如特征共线性、高维稀疏性和数据不足），代理模型可能无法在所有场景中有效学习。本文提出AltFS，一种面向深度推荐系统的轻代理特征选择方法。AltFS将大语言模型的语义推理能力与代理模型的特定任务学习相结合。首先，大语言模型生成特征重要性的语义排序，随后由代理模型结合世界知识与任务特定洞察进行优化。在三个真实推荐平台公开数据集上的大量实验验证了AltFS的有效性。我们的代码已公开以确保可复现性。