Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existing methods have undertaken tailored refinements to the diffusion and reverse process. However, these approaches typically use the highest-score item in corpus for user interest prediction, leading to the ignorance of the user's generalized preference contained within other items, thereby remaining constrained by the data sparsity issue. To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization. Extensive experiments and analyses on four datasets have verified the superiority of the proposed PDRec over the state-of-the-art baselines and showcased the universality of PDRec as a flexible plugin for commonly-used sequential encoders in different recommendation scenarios. The code is available in https://github.com/hulkima/PDRec.

翻译：开创性工作已证实扩散模型在探索推荐中的信息不确定性方面具有有效性。考虑到推荐任务与图像合成任务之间的差异，现有方法对扩散和反向过程进行了针对性改进。然而，这些方法通常使用语料库中得分最高的物品进行用户兴趣预测，导致忽略其他物品中包含的用户泛化偏好，从而仍受限于数据稀疏问题。为解决该问题，本文提出一种新颖的插件式推荐扩散模型框架（PDRec），该框架将扩散模型作为灵活插件，联合充分利用扩散生成的所有物品上的用户偏好。具体而言，PDRec首先通过时间间隔扩散模型推断用户对所有物品的动态偏好，并提出历史行为重加权机制（HBR）以识别高质量行为并抑制噪声行为。除观测物品外，PDRec提出基于扩散的正向增强策略（DPA），利用排名靠前的未观测物品作为潜在正样本，引入信息丰富且多样的软信号以缓解数据稀疏。为减轻假负采样问题，PDRec采用无噪声负采样机制（NNS）选择稳定负样本，确保模型有效优化。在四个数据集上的大量实验与分析验证了所提出的PDRec相较于最先进基准方法的优越性，并展示了PDRec作为灵活插件在不同推荐场景中广泛适用于常用序列编码器的通用性。代码已开源在 https://github.com/hulkima/PDRec。