Diffusion models, known for their strong generative capability derived from iterative noising and denoising processes, have recently emerged as a promising paradigm for sequential recommendation. To incorporate user history for personalization, existing methods typically follow a history-guided denoising paradigm inspired by text-guided image generation, where target item representations are reconstructed from Gaussian noise conditioned on user historical interactions. However, this design remains fundamentally anchored to an "item $\leftrightarrow$ noise" formulation, introducing an additional noise-reconstruction burden that may distract the model from capturing user-specific preference structures. Motivated by this limitation, we revisit diffusion-based sequential recommendation from a preference-centric perspective and adopt a preference bridging design that enables a direct "item $\leftrightarrow$ history" transition instead of relying on Gaussian noise. Based on this idea, we propose Brownian Bridge Diffusion Recommendation (BBDRec), which leverages the Brownian bridge process to construct a structured diffusion trajectory between target items and user historical representations, thereby better aligning diffusion modeling with the intrinsic nature of recommendation. Extensive experiments on multiple public datasets show that BBDRec consistently outperforms representative sequential and diffusion-based recommendation baselines. The implementation code is publicly available at https://github.com/baiyimeng/BBDRec.

翻译：扩散模型凭借其通过迭代加噪与去噪过程所展现的强生成能力，近期已成为序列推荐领域一种有前景的范式。为融入用户历史信息以实现个性化，现有方法通常遵循一种受文本引导图像生成启发的历史引导去噪范式，即在以用户历史交互为条件的情况下，从高斯噪声中重构目标项目表示。然而，这一设计从根本上仍固守于“项目 ↔ 噪声”的公式化表述，引入了额外的噪声重构负担，可能分散模型对用户特定偏好结构的捕捉能力。受此局限的驱动，我们从偏好中心视角重新审视基于扩散的序列推荐，并采用偏好桥接设计，实现从依赖高斯噪声到直接“项目 ↔ 历史”的过渡。基于这一思想，我们提出了布朗桥扩散推荐（Brownian Bridge Diffusion Recommendation, BBDRec），该方法利用布朗桥过程构建目标项目与用户历史表示之间的结构化扩散轨迹，从而更好地将扩散建模与推荐的固有本质对齐。在多个公开数据集上的大量实验表明，BBDRec在代表性序列推荐与基于扩散的推荐基线方法上均持续取得更优表现。实现代码已公开于 https://github.com/baiyimeng/BBDRec。