通过反事实推理指南中和基于大语言模型的推荐中的流行度偏差
Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines
AI总结：
针对大语言模型推荐中的流行度偏差，提出模型无关框架NPRec，通过反事实细化生成去偏文本指南，在推理时引导模型，无需参数更新，在三个数据集上提升了推荐准确性、解释质量和去偏能力。
摘要：
在生成式人工智能时代，推荐系统正从精确预测转向可信生成。大语言模型（LLM）通过推断用户兴趣并生成自然语言解释来支持这一转变。然而，基于大语言模型的推荐面临一个根本性障碍：流行度偏差。通过在大型语料库上的预训练，大语言模型倾向于依赖全局统计和趋势信号，从而产生遵循流行度而非真实偏好的推荐。由于这种偏差纠缠在模型参数中且难以直接移除，我们提出了通过反事实推理指南中和基于大语言模型的推荐中的流行度偏差（NPRec），这是一种通过外部语义干预来缓解流行度偏差的模型无关框架。NPRec执行反事实细化，以因果方式将内在用户兴趣与流行度驱动的从众行为分离，生成反映真实用户偏好的去偏文本指南。这些指南在推理时注入，将大语言模型从无约束生成转变为引导推理，无需任何参数更新。作为显式前提，它们既为忠实的解释提供依据，又提高了推荐质量。在三个真实世界数据集上的大量实验表明，NPRec在推荐准确性、解释质量和去偏能力方面均取得了有前景的性能。
英文摘要：
In the era of generative AI, recommender systems are moving from precise prediction to trustworthy generation. Large language models (LLMs) support this shift by inferring user interests and producing natural-language explanations. However, LLM-based recommendation suffers from a fundamental obstacle: popularity bias. Through pre-training on massive corpora, LLMs tend to rely on global statistics and trend signals, yielding recommendations that follow popularity rather than genuine preference. As this bias is entangled in model parameters and is hard to remove directly, we propose Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines (NPRec), a model-agnostic framework that mitigates popularity bias through external semantic intervention. NPRec performs counterfactual refinement to causally separate intrinsic user interests from popularity-driven conformity, producing debiased textual guidelines that reflect actual user preferences. These guidelines are injected at inference time to shift the LLM from unconstrained generation to guided reasoning, without any parameter updates. Serving as explicit premises, they both ground faithful explanations and improve recommendation quality. Extensive experiments on three real-world datasets demonstrate that NPRec achieves promising performance in recommendation accuracy, explanation quality, and debiasing capability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
