Generative models, such as Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN), have been successfully applied in sequential recommendation. These methods require sampling from probability distributions and adopt auxiliary loss functions to optimize the model, which can capture the uncertainty of user behaviors and alleviate exposure bias. However, existing generative models still suffer from the posterior collapse problem or the model collapse problem, thus limiting their applications in sequential recommendation. To tackle the challenges mentioned above, we leverage a new paradigm of the generative models, i.e., diffusion models, and present sequential recommendation with diffusion models (DiffRec), which can avoid the issues of VAE- and GAN-based models and show better performance. While diffusion models are originally proposed to process continuous image data, we design an additional transition in the forward process together with a transition in the reverse process to enable the processing of the discrete recommendation data. We also design a different noising strategy that only noises the target item instead of the whole sequence, which is more suitable for sequential recommendation. Based on the modified diffusion process, we derive the objective function of our framework using a simplification technique and design a denoise sequential recommender to fulfill the objective function. As the lengthened diffusion steps substantially increase the time complexity, we propose an efficient training strategy and an efficient inference strategy to reduce training and inference cost and improve recommendation diversity. Extensive experiment results on three public benchmark datasets verify the effectiveness of our approach and show that DiffRec outperforms the state-of-the-art sequential recommendation models.

翻译：生成式模型，如变分自编码器（VAE）和生成对抗网络（GAN），已成功应用于序列推荐任务中。这些方法需要从概率分布中采样，并采用辅助损失函数优化模型，从而捕捉用户行为的不确定性并缓解曝光偏差。然而，现有生成式模型仍存在后验坍缩问题或模型坍缩问题，限制了其在序列推荐中的应用。为解决上述挑战，我们采用生成式模型的新范式——扩散模型（diffusion models），提出了基于扩散模型的序列推荐方法（DiffRec），该方法可避免VAE和GAN类模型的缺陷，并展现出更优性能。尽管扩散模型最初是为处理连续图像数据而设计的，但我们在前向过程中额外设计了一个转移步骤，并在反向过程中相应设计了一个转移步骤，使其能够处理离散的推荐数据。我们还设计了一种不同的噪声添加策略，仅对目标物品而非整个序列添加噪声，这更适用于序列推荐场景。基于改进后的扩散过程，我们通过简化技术推导出框架的目标函数，并设计了一个去噪序列推荐器来实现该目标函数。针对扩散步长增加导致时间复杂度显著提升的问题，我们提出了高效训练策略与高效推理策略，以降低训练和推理成本并提升推荐多样性。在三个公开基准数据集上的大量实验验证了该方法有效性，结果表明DiffRec优于当前最先进的序列推荐模型。