标题： 基于扩散的推荐系统的效率-效果权衡 显示英文标题

标题： On Efficiency-Effectiveness Trade-off of Diffusion-based Recommenders

摘要： 扩散模型已成为生成序列推荐的强大范式，通常通过多步骤去噪过程，根据用户交互历史生成要推荐的下一个项目。 然而，多步骤过程依赖于离散近似，引入了离散化误差，这在计算效率和推荐效果之间形成了权衡。 为了解决这一权衡，我们提出了TA-Rec，这是一个两阶段框架，在预训练期间通过平滑去噪函数实现一步生成，同时在微调期间通过与用户偏好对齐来缓解轨迹偏差。 具体而言，为了在不牺牲推荐性能的情况下提高效率，TA-Rec使用时间一致性正则化（TCR）对去噪模型进行预训练，强制相邻步骤之间的去噪结果保持一致性。 因此，我们可以平滑去噪函数，以有限的误差将噪声映射为理想项目。 为进一步提高效果，TA-Rec引入了自适应偏好对齐（APA），该方法基于偏好对相似性和时间步数，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由离散化误差引起的权衡，提高了基于扩散的推荐器的效率和效果。 显示英文摘要

摘要： 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.