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.
@article{arxiv.2412.08516,
title = {SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems},
author = {Pengyue Jia and Zhaocheng Du and Yichao Wang and Xiangyu Zhao and Xiaopeng Li and Yuhao Wang and Qidong Liu and Huifeng Guo and Ruiming Tang},
journal= {arXiv preprint arXiv:2412.08516},
year = {2025}
}