Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). Existing approaches predominantly rely on implicit conditional diffusion models, which compress user behaviors into a single representation during the forward diffusion process. While effective to some extent, this oversimplification often leads to the loss of sequential and contextual information, which is critical for understanding user behavior. Moreover, explicit information, such as user-item interactions or sequential patterns, remains underutilized, despite its potential to directly guide the recommendation process and improve precision. However, combining implicit and explicit information is non-trivial, as it requires dynamically integrating these complementary signals while avoiding noise and irrelevant patterns within user behaviors. To address these challenges, we propose Dual Conditional Diffusion Models for Sequential Recommendation (DCRec), which effectively integrates implicit and explicit information by embedding dual conditions into both the forward and reverse diffusion processes. This allows the model to retain valuable sequential and contextual information while leveraging explicit user-item interactions to guide the recommendation process. Specifically, we introduce the Dual Conditional Diffusion Transformer (DCDT), which employs a cross-attention mechanism to dynamically integrate explicit signals throughout the diffusion stages, ensuring contextual understanding and minimizing the influence of irrelevant patterns. This design enables precise and contextually relevant recommendations. Extensive experiments on public benchmark datasets demonstrate that DCRec significantly outperforms state-of-the-art methods in both accuracy and computational efficiency.

翻译：近年来，扩散模型在序列推荐领域取得了显著进展。现有方法主要依赖于隐式条件扩散模型，这类模型在前向扩散过程中将用户行为压缩为单一表征。尽管这种方法在一定程度上有效，但过度简化往往导致对理解用户行为至关重要的序列信息与上下文信息的丢失。此外，显式信息（如用户-物品交互或序列模式）的潜力尚未被充分挖掘——这类信息本可直接指导推荐过程并提升推荐精度。然而，隐式与显式信息的有效融合并非易事，因为这需要在避免用户行为中噪声与无关模式干扰的同时，动态整合这些互补信号。为解决上述挑战，本文提出用于序列推荐的双条件扩散模型，该模型通过在前向与反向扩散过程中嵌入双重条件，实现了隐式与显式信息的有效融合。这使得模型在保留有价值的序列与上下文信息的同时，能够利用显式的用户-物品交互来指导推荐过程。具体而言，我们设计了双条件扩散Transformer，该架构采用交叉注意力机制在扩散各阶段动态整合显式信号，从而确保对上下文的理解并最小化无关模式的影响。这一设计使得推荐结果兼具精确性与上下文相关性。在多个公开基准数据集上的大量实验表明，本方法在推荐精度与计算效率方面均显著优于现有最先进方法。