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：主流序列推荐（SR）解决方案将物品表示为固定向量。这些向量在捕捉物品潜在方面及用户多样化偏好方面能力有限。作为一种新型生成范式，扩散模型在计算机视觉和自然语言处理等领域已取得卓越表现。我们认为，其在表示生成方面的独特优势恰好契合序列推荐的问题设定。本文首次尝试将扩散模型适配至SR领域，并提出DiffuRec，用于物品表示构建与不确定性注入。不同于将物品表征为固定向量，DiffuRec将其建模为分布，以自适应反映用户的多重兴趣与物品的多种维度。在扩散阶段，DiffuRec通过噪声添加将目标物品嵌入转化为高斯分布，进而用于序列化物品分布表示生成与不确定性注入；随后将物品表示输入近似器（Approximator）进行目标物品表示重建。在逆扩散阶段，基于用户历史交互行为，我们将高斯噪声逆转为目标物品表示，并通过取整操作预测目标物品。在四个数据集上的实验表明，DiffuRec以显著优势超越强基线模型。