Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under target behavior for the next interacted item prediction. Unlike previous methods that adopt unidirectional modeling by mapping auxiliary behaviors to target behavior, recent concerns are shifting from behavior-fixed to behavior-specific recommendation. However, these methods still ignore the user's latent preference that underlying decision-making, leading to suboptimal solutions. Meanwhile, due to the asymmetric deterministic between items and behaviors, discriminative paradigm based on preference scoring is unsuitable to capture the uncertainty from low-entropy behaviors to high-entropy items, failing to provide efficient and diverse recommendation. To address these challenges, we propose \textbf{FatsMB}, a framework based diffusion model that guides preference generation \textit{\textbf{F}rom Behavior-\textbf{A}gnostic \textbf{T}o Behavior-\textbf{S}pecific} in latent spaces, enabling diverse and accurate \textit{\textbf{M}ulti-\textbf{B}ehavior Sequential Recommendation}. Specifically, we design a Multi-Behavior AutoEncoder (MBAE) to construct a unified user latent preference space, facilitating interaction and collaboration across Behaviors, within Behavior-aware RoPE (BaRoPE) employed for multiple information fusion. Subsequently, we conduct target behavior-specific preference transfer in the latent space, enriching with informative priors. A Multi-Condition Guided Layer Normalization (MCGLN) is introduced for the denoising. Extensive experiments on real-world datasets demonstrate the effectiveness of our model.

翻译：多行为序列推荐（MBSR）旨在学习用户多行为序列的动态异质交互，从而捕捉目标行为下的用户偏好，以预测下一个交互项目。与以往通过将辅助行为映射到目标行为进行单向建模的方法不同，当前的研究焦点正从行为固定的推荐转向行为特定的推荐。然而，这些方法仍忽略了用户潜在偏好这一底层决策因素，导致次优解。同时，由于项目与行为间存在不对称确定性，基于偏好评分的判别式范式难以捕捉从低熵行为到高熵项目的不确定性，无法提供高效且多样化的推荐。为应对这些挑战，我们提出 \textbf{FatsMB}，一种基于扩散模型的框架，在潜在空间中引导偏好生成 \textit{\textbf{F}rom Behavior-\textbf{A}gnostic \textbf{T}o Behavior-\textbf{S}pecific}，以实现多样化且准确的 \textit{\textbf{M}ulti-\textbf{B}ehavior Sequential Recommendation}。具体而言，我们设计了一个多行为自动编码器（MBAE）来构建统一的用户潜在偏好空间，在采用行为感知旋转位置编码（BaRoPE）进行多信息融合的基础上，促进跨行为及行为内的交互与协作。随后，我们在潜在空间中进行目标行为特定的偏好迁移，并融入信息丰富的先验知识。我们引入了多条件引导层归一化（MCGLN）进行去噪。在真实世界数据集上的大量实验证明了我们模型的有效性。