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

• URL: http://arxiv.org/abs/2412.08516v2
• Date: Wed, 27 Aug 2025 16:33:34 GMT
• Title: SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
• Authors: Pengyue Jia, Zhaocheng Du, Yichao Wang, Xiangyu Zhao, Xiaopeng Li, Yuhao Wang, Qidong Liu, Huifeng Guo, Ruiming Tang,
• Abstract summary: SurrogatE-Light Feature selection method for deep recommender systems.<n> SELF integrates semantic reasoning from Large Language Models with task-specific learning from surrogate models.<n> Comprehensive experiments on three public datasets from real-world recommender platforms validate the effectiveness of SELF.
• Score: 51.09233156090496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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