Mainstream solutions to Sequential Recommendation (SR) represent items with fixed vectors. These vectors have limited capability in capturing items' latent aspects and users' diverse preferences. As a new generative paradigm, Diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this paper, we make the very first attempt to adapt Diffusion model to SR and propose DiffuRec, for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec, which reflect user's multiple interests and item's various aspects adaptively. In diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterwards, the item representation is fed into an Approximator for target item representation reconstruction. In reversion phase, based on user's historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin.

翻译：序列推荐（SR）的主流解决方案使用固定向量表示物品。这些向量在捕获物品的潜在方面和用户的多样化偏好方面能力有限。作为一种新的生成范式，扩散模型在计算机视觉和自然语言处理等领域取得了优异性能。据我们所知，其在表征生成方面的独特优势与序列推荐的问题设置高度契合。本文首次尝试将扩散模型适配至SR，并提出DiffuRec，用于物品表示构建和不确定性注入。不同于将物品表示建模为固定向量，我们在DiffuRec中将其表示为分布，从而自适应地反映用户的多重兴趣和物品的多种侧面。在扩散阶段，DiffuRec通过噪声添加将目标物品嵌入退化为高斯分布，进而用于序列物品分布表示生成和不确定性注入。随后，物品表示被输入近似器以进行目标物品表示重构。在逆扩散阶段，基于用户的历史交互行为，我们将高斯噪声逆向为目标物品表示，然后通过取整操作进行目标物品预测。在四个数据集上的实验表明，DiffuRec以较大优势超越了强基线方法。