Diversity control is an important task to alleviate bias amplification and filter bubble problems. The desired degree of diversity may fluctuate based on users' daily moods or business strategies. However, existing methods for controlling diversity often lack flexibility, as diversity is decided during training and cannot be easily modified during inference. We propose \textbf{D3Rec} (\underline{D}isentangled \underline{D}iffusion model for \underline{D}iversified \underline{Rec}ommendation), an end-to-end method that controls the accuracy-diversity trade-off at inference. D3Rec meets our three desiderata by (1) generating recommendations based on category preferences, (2) controlling category preferences during the inference phase, and (3) adapting to arbitrary targeted category preferences. In the forward process, D3Rec removes category preferences lurking in user interactions by adding noises. Then, in the reverse process, D3Rec generates recommendations through denoising steps while reflecting desired category preferences. Extensive experiments on real-world and synthetic datasets validate the effectiveness of D3Rec in controlling diversity at inference.

翻译：多样性控制是缓解偏见放大和过滤气泡问题的重要任务。期望的多样性程度可能随用户日常情绪或商业策略而波动。然而，现有的多样性控制方法通常缺乏灵活性，因为多样性在训练阶段就已确定，无法在推理阶段轻松调整。我们提出 \textbf{D3Rec} (\underline{D}isentangled \underline{D}iffusion model for \underline{D}iversified \underline{Rec}ommendation)，一种在推理阶段控制准确性与多样性权衡的端到端方法。D3Rec 通过以下三点满足我们的设计目标：(1) 基于类别偏好生成推荐，(2) 在推理阶段控制类别偏好，(3) 适配任意目标类别偏好。在前向过程中，D3Rec 通过添加噪声消除用户交互中潜在的类别偏好。随后，在反向过程中，D3Rec 通过去噪步骤生成推荐，同时反映期望的类别偏好。在真实世界和合成数据集上的大量实验验证了 D3Rec 在推理阶段控制多样性的有效性。