Diffusion models have emerged as a powerful paradigm for generative sequential recommendation, which typically generate next items to recommend guided by user interaction histories with a multi-step denoising process. However, the multi-step process relies on discrete approximations, introducing discretization error that creates a trade-off between computational efficiency and recommendation effectiveness. To address this trade-off, we propose TA-Rec, a two-stage framework that achieves one-step generation by smoothing the denoising function during pretraining while alleviating trajectory deviation by aligning with user preferences during fine-tuning. Specifically, to improve the efficiency without sacrificing the recommendation performance, TA-Rec pretrains the denoising model with Temporal Consistency Regularization (TCR), enforcing the consistency between the denoising results across adjacent steps. Thus, we can smooth the denoising function to map the noise as oracle items in one step with bounded error. To further enhance effectiveness, TA-Rec introduces Adaptive Preference Alignment (APA) that aligns the denoising process with user preference adaptively based on preference pair similarity and timesteps. Extensive experiments prove that TA-Rec's two-stage objective effectively mitigates the discretization errors-induced trade-off, enhancing both efficiency and effectiveness of diffusion-based recommenders.

翻译：扩散模型已成为生成式序列推荐的有力范式，通常通过多步去噪过程，依据用户交互历史生成待推荐的下一个物品。然而，多步过程依赖于离散近似，引入了离散化误差，从而在计算效率与推荐效果之间形成权衡。为解决这一权衡，我们提出TA-Rec，一个两阶段框架：通过在预训练阶段平滑去噪函数实现一步生成，同时在微调阶段通过与用户偏好对齐来减轻轨迹偏差。具体而言，为在不牺牲推荐性能的前提下提升效率，TA-Rec采用时序一致性正则化（TCR）预训练去噪模型，强制相邻步骤间去噪结果的一致性。由此，我们能够平滑去噪函数，以有界误差一步将噪声映射为目标物品。为进一步提升效果，TA-Rec引入自适应偏好对齐（APA），基于偏好对相似性和时间步自适应地将去噪过程与用户偏好对齐。大量实验证明，TA-Rec的两阶段目标有效缓解了离散化误差引起的权衡问题，同时提升了扩散推荐系统的效率与效果。