Simple Yet Effective Margin Semantic Extraction for Large Language Models enhanced Recommender Systems

Recent advances in large language models (LLMs) have introduced rich semantic information into recommender systems. However, LLMs-derived semantics are not inherently compatible with collaborative signals, making it difficult to fully exploit their benefits and potentially causing information distortion. To address this issue, we shift from fusion or alignment to extraction and propose a model-agnostic framework, MarRec, which imposes a lightweight margin constraint as an implicit information bottleneck to effectively extract useful semantics. Additionally, we design two instance margin variants and verify that fine-grained constraints can further improve useful semantic extraction. Extensive experiments demonstrate that MarRec outperforms baselines while maintaining high efficiency and showing strong robustness under data sparsity, long-tail, and cold-start scenarios.