Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation

• URL: http://arxiv.org/abs/2508.10312v1
• Date: Thu, 14 Aug 2025 03:33:02 GMT
• Title: Beyond Semantic Understanding: Preserving Collaborative Frequency Components in LLM-based Recommendation
• Authors: Minhao Wang, Yunhang He, Cong Xu, Zhangchi Zhu, Wei Zhang,
• Abstract summary: FreLLM4Rec is an approach designed to balance semantic and collaborative information from a spectral perspective.<n>Experiments on four benchmark datasets demonstrate that FreLLM4Rec successfully mitigates collaborative signal attenuation.
• Score: 7.014265360936046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. However, LLM-based recommenders exhibit a tendency to overemphasize semantic correlations within users' interaction history. When taking pretrained collaborative ID embeddings as input, LLM-based recommenders progressively weaken the inherent collaborative signals as the embeddings propagate through LLM backbones layer by layer, as opposed to traditional Transformer-based sequential models in which collaborative signals are typically preserved or even enhanced for state-of-the-art performance. To address this limitation, we introduce FreLLM4Rec, an approach designed to balance semantic and collaborative information from a spectral perspective. Item embeddings that incorporate both semantic and collaborative information are first purified using a Global Graph Low-Pass Filter (G-LPF) to preliminarily remove irrelevant high-frequency noise. Temporal Frequency Modulation (TFM) then actively preserves collaborative signal layer by layer. Note that the collaborative preservation capability of TFM is theoretically guaranteed by establishing a connection between the optimal but hard-to-implement local graph fourier filters and the suboptimal yet computationally efficient frequency-domain filters. Extensive experiments on four benchmark datasets demonstrate that FreLLM4Rec successfully mitigates collaborative signal attenuation and achieves competitive performance, with improvements of up to 8.00\% in NDCG@10 over the best baseline. Our findings provide insights into how LLMs process collaborative information and offer a principled approach for improving LLM-based recommendation systems.

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