Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation.

翻译：序列推荐（SRS）近年来已成为许多应用的技术基础，其目标是根据用户的历史交互行为推荐下一个项目。然而，序列推荐常面临数据稀疏性问题，该问题在推荐系统中普遍存在。此外，大多数用户仅与少量项目交互，但现有SRS模型对这些用户的表现往往不佳。这种被称为长尾用户问题的问题仍有待解决。数据增强是缓解这两个问题的独特方法，但其通常需要精心设计的训练策略，或受限于生成交互的质量低下。为解决这些问题，我们提出了面向序列推荐的扩散增强方法（DiffuASR），以实现更高质量的生成。通过DiffuASR增强的数据集可直接用于训练序列推荐模型，无需复杂的训练流程。为充分利用扩散模型的生成能力，我们首先提出基于扩散的伪序列生成框架，以弥合图像生成与序列生成之间的差距。随后，设计了序列化U-Net结构，使扩散噪声预测模型U-Net适应离散序列生成任务。最后，我们开发了两种引导策略，使生成序列与原始序列的偏好趋于一致。为验证所提出的DiffuASR，我们在三个真实数据集上结合三种序列推荐模型进行了大量实验。实验结果证明了DiffuASR的有效性。据我们所知，DiffuASR是首个将扩散模型引入推荐领域的开创性工作。