用大模型语义推理+轻量代理模型，提升推荐系统特征选择效果
SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
- 结合大模型语义理解与代理模型，实现更精准的特征重要性排序
- 在三个真实平台数据集上显著提升推荐性能，优于传统方法
- 适合需要高效特征筛选的工业级推荐系统研发人员
特征选择对提升推荐系统的模型效率和预测性能至关重要。传统方法通常依赖决策树或神经网络等代理模型来估计特征重要性，但这些模型在特征共线性、高维稀疏性和数据不足等次优训练条件下表现受限。本文提出SELF（Surrogate-light Feature Selection），一种面向深度推荐系统的代理轻量特征选择方法。SELF将大语言模型（LLM）的语义推理能力与代理模型的任务特定学习相结合：首先由LLM生成基于语义的特征重要性排序，再由代理模型进行精细化调整，从而融合通用世界知识与任务相关学习。在三个来自真实推荐平台的公开数据集上进行的全面实验验证了SELF的有效性。
原文摘要 · Abstract (English)
Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Conventional approaches typically employ surrogate models-such as decision trees or neural networks-to estimate feature importance. However, their effectiveness is inherently constrained, as these models may struggle under suboptimal training conditions, including feature collinearity, high-dimensional sparsity, and insufficient data. In this paper, we propose SELF, an SurrogatE-Light Feature selection method for deep recommender systems. SELF integrates semantic reasoning from Large Language Models (LLMs) with task-specific learning from surrogate models. Specifically, LLMs first produce a semantically informed ranking of feature importance, which is subsequently refined by a surrogate model, effectively integrating general world knowledge with task-specific learning. Comprehensive experiments on three public datasets from real-world recommender platforms validate the effectiveness of SELF.
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