Mainstream solutions to sequential recommendation 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 article, we make the very first attempt to adapt the diffusion model to sequential recommendation 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 a user’s multiple interests and an item’s various aspects adaptively. In the 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. Afterward, the item representation is fed into an approximator for target item representation reconstruction. In the reverse phase, based on a user’s historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply a rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin.¹

顺序推荐的主流解决方案表示具有固定向量的项目。这些向量在捕获项目的潜在方面和用户的不同偏好方面的能力有限。扩散模型作为一种新的生成范式，在计算机视觉、自然语言处理等领域取得了优异的性能。据我们了解，其在表示生成方面的独特优点非常适合顺序推荐的问题设置。在本文中，我们首次尝试将扩散模型应用于顺序推荐，并提出用于项目表示构建和不确定性注入的DiffuRec 。我们没有将项目表示建模为固定向量，而是将它们表示为DiffuRec中的分布，它自适应地反映了用户的多种兴趣和项目的各个方面。在扩散阶段，DiffuRec通过添加噪声将目标项嵌入高斯分布，进一步应用于顺序项分布表示生成和不确定性注入。之后，项目表示被输入到近似器中以进行目标项目表示重建。在反向阶段，基于用户的历史交互行为，我们将高斯噪声反转为目标项目表示，然后应用舍入操作来进行目标项目预测。对四个数据集的实验表明，DiffuRec 的性能大幅优于强基线。¹