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arXiv · 2412.08516

SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems

Pengyue Jia·Zhaocheng Du·Yichao Wang·Xiangyu Zhao·Xiaopeng Li·Yuhao Wang·Qidong Liu·Huifeng Guo·Ruiming Tang
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Abstract

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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Pengyue Jia, Zhaocheng Du, Yichao Wang, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Qidong Liu, Huifeng Guo, Ruiming Tang. 2024-12-11. SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems. https://arxiv.org/abs/2412.08516

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