Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy interactions, which significantly impairs recommendation performance. Accurately identifying such noisy interactions without additional information is particularly challenging due to the absence of explicit supervisory signals indicating noise. Large Language Models (LLMs), equipped with extensive open knowledge and semantic reasoning abilities, offer a promising avenue to bridge this information gap. However, employing LLMs for denoising in sequential recommendation presents notable challenges: 1) Direct application of pretrained LLMs may not be competent for the denoising task, frequently generating nonsensical responses; 2) Even after fine-tuning, the reliability of LLM outputs remains questionable, especially given the complexity of the denoising task and the inherent hallucinatory issue of LLMs. To tackle these challenges, we propose LLM4DSR, a tailored approach for denoising sequential recommendation using LLMs. We constructed a self-supervised fine-tuning task to activate LLMs' capabilities to identify noisy items and suggest replacements. Furthermore, we developed an uncertainty estimation module that ensures only high-confidence responses are utilized for sequence corrections. Remarkably, LLM4DSR is model-agnostic, allowing corrected sequences to be flexibly applied across various recommendation models. Extensive experiments validate the superiority of LLM4DSR over existing methods.

翻译：序列推荐系统根据用户的历史交互序列生成推荐。然而，在实践中，这些收集到的序列常常受到噪声交互的污染，这会显著损害推荐性能。由于缺乏指示噪声的显式监督信号，在不依赖额外信息的情况下准确识别此类噪声交互尤其具有挑战性。大语言模型具备丰富的开放知识和语义推理能力，为弥合这一信息鸿沟提供了一条有前景的途径。然而，将LLMs应用于序列推荐去噪面临显著挑战：1）直接应用预训练的LLMs可能无法胜任去噪任务，经常产生无意义的响应；2）即使在微调之后，LLM输出的可靠性仍然存疑，特别是考虑到去噪任务的复杂性以及LLMs固有的幻觉问题。为了应对这些挑战，我们提出了LLM4DSR，一种利用LLMs进行序列推荐去噪的定制化方法。我们构建了一个自监督微调任务，以激活LLMs识别噪声项目并提出替换建议的能力。此外，我们开发了一个不确定性估计模块，确保仅将高置信度的响应用于序列校正。值得注意的是，LLM4DSR是模型无关的，允许校正后的序列灵活地应用于各种推荐模型。大量实验验证了LLM4DSR相对于现有方法的优越性。