Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions. However, two prominent generative models, namely Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs), exhibit challenges that impede achieving optimal performance in sequential recommendation tasks. Specifically, GANs suffer from unstable optimization, while VAEs are prone to posterior collapse and over-smoothed generations. The sparse and noisy nature of sequential recommendation further exacerbates these issues. In response to these limitations, we present a conditional denoising diffusion model, which includes a sequence encoder, a cross-attentive denoising decoder, and a step-wise diffuser. This approach streamlines the optimization and generation process by dividing it into easier and tractable steps in a conditional autoregressive manner. Furthermore, we introduce a novel optimization schema that incorporates both cross-divergence loss and contrastive loss. This novel training schema enables the model to generate high-quality sequence/item representations and meanwhile precluding collapse. We conducted comprehensive experiments on four benchmark datasets, and the superior performance achieved by our model attests to its efficacy.

翻译：生成模型因其通过学习固有数据分布来处理不确定性的能力而备受关注。然而，两种主流生成模型——生成对抗网络（GANs）和变分自编码器（VAEs）——在序列推荐任务中面临挑战，难以实现最优性能。具体而言，GANs存在优化不稳定的问题，而VAEs则易出现后验坍塌和生成结果过度平滑的现象。序列推荐数据的稀疏性和噪声特性进一步加剧了这些问题。针对这些局限性，我们提出了一种条件去噪扩散模型，该模型包含序列编码器、交叉注意力去噪解码器和逐步扩散器。该方法通过将优化与生成过程分解为条件自回归方式下更简单、更易处理的步骤，从而简化了流程。此外，我们引入了一种融合交叉散度损失和对比损失的新型优化方案。这一新颖的训练方案使模型能够生成高质量的序列/物品表示，同时避免坍塌问题。我们在四个基准数据集上进行了全面实验，模型所取得的优越性能证明了其有效性。