Recent advancements in diffusion models have shown promising results in sequential recommendation (SR). However, current diffusion-based methods still exhibit two key limitations. First, they implicitly model the diffusion process for target item embeddings rather than the discrete target item itself, leading to inconsistency in the recommendation process. Second, existing methods rely on either implicit or explicit conditional diffusion models, limiting their ability to fully capture the context of user behavior and leading to less robust target item embeddings. In this paper, we propose the Dual Conditional Diffusion Models for Sequential Recommendation (DCRec), introducing a discrete-to-continuous sequential recommendation diffusion framework. Our framework introduces a complete Markov chain to model the transition from the reversed target item representation to the discrete item index, bridging the discrete and continuous item spaces for diffusion models and ensuring consistency with the diffusion framework. Building on this framework, we present the Dual Conditional Diffusion Transformer (DCDT) that incorporates the implicit conditional and the explicit conditional for diffusion-based SR. Extensive experiments on public benchmark datasets demonstrate that DCRec outperforms state-of-the-art methods.

翻译：近年来，扩散模型在序列推荐领域取得了令人瞩目的进展。然而，现有的基于扩散的方法仍存在两个关键局限性。首先，这些方法隐式地对目标物品嵌入的扩散过程进行建模，而非直接对离散的目标物品本身进行建模，导致推荐过程存在不一致性。其次，现有方法依赖于隐式条件扩散模型或显式条件扩散模型，限制了其充分捕捉用户行为上下文的能力，从而导致生成的目标物品嵌入鲁棒性不足。本文提出用于序列推荐的双条件扩散模型，引入了一种离散到连续的序列推荐扩散框架。该框架通过构建完整的马尔可夫链来建模从反向目标物品表示到离散物品索引的转换过程，从而在扩散模型中桥接了离散与连续的物品空间，并确保了与扩散框架的一致性。基于此框架，我们进一步提出了双条件扩散Transformer，该模型融合了隐式条件与显式条件以支持基于扩散的序列推荐。在多个公开基准数据集上的大量实验表明，本方法优于当前最先进的推荐模型。