用大模型生成更精准的推荐负样本，提升模型收敛速度与效果
Hard Negative Sampling via Large Language Models for Recommendation
- 利用大模型语义理解能力生成行为差异但语义相关负样本
- 在多个真实数据集上显著超越传统方法与基线模型
- 有效缓解热门物品偏差和数据稀疏问题，适合推荐系统研究者
硬负样本采样能加速推荐模型收敛并强化决策边界，但现有方法多依赖启发式策略，从固定候选池中选取负样本。由于缺乏语义感知，常将符合用户语义兴趣的物品误判为负样本，导致虚假硬负样本（FHNS），引入噪声监督，阻碍模型最优性能。为此，我们提出HNLMRec，一种基于大语言模型的生成式语义负样本采样框架。该框架借助大模型的语义推理能力，直接生成行为上相异但语义上与用户偏好相关的负样本。此外，通过监督微调将协同过滤信号融入大模型，引导其生成更可靠、更具信息量的硬负样本。在多个真实世界数据集上的大量实验表明，HNLMRec显著优于传统方法与大模型增强基线，同时有效缓解流行度偏差与数据稀疏性，提升泛化能力。
原文摘要 · Abstract (English)
Hard negative sampling improves recommendation performance by accelerating convergence and sharpening the decision boundary. However, most existing methods rely on heuristic strategies, selecting negatives from a fixed candidate pool. Lacking semantic awareness, these methods often misclassify items that align with users' semantic interests as negatives, resulting in False Hard Negative Samples (FHNS). Such FHNS inject noisy supervision and hinder the model's optimal performance. To address this challenge, we propose HNLMRec, a generative semantic negative sampling framework. Leveraging the semantic reasoning capabilities of Large Language Models (LLMs), HNLMRec directly generates negative samples that are behaviorally distinct yet semantically relevant with respect to user preferences. Furthermore, we integrate collaborative filtering signals into the LLM via supervised fine-tuning, guiding the model to synthesize more reliable and informative hard negatives. Extensive experiments on multiple real-world datasets demonstrate that HNLMRec significantly outperforms traditional methods and LLM-enhanced baselines, while effectively mitigating popularity bias and data sparsity, thereby improving generalization.
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