摘要： 近年来，越来越多的研究开始关注如何基于用户行为建模多兴趣，以刻画用户的复杂偏好。然而，在缺乏物品类别等外部辅助信息的隐式建模场景中，多兴趣模型难以准确区分不同行为的兴趣归属，容易将与目标兴趣关联较弱甚至无关的物品错误聚合到同一兴趣中，形成兴趣特定噪声。为缓解这一问题，本文提出了一种两阶段去噪多兴趣推荐算法（DMIRec），从物品特征和兴趣表示两个层面抑制兴趣特定噪声。在物品降噪阶段，利用可学习的滤波器对各兴趣中的无关物品特征进行滤波，得到每个兴趣的降噪序列；在兴趣去噪阶段，引入条件扩散模型，以与当前兴趣高度相关的物品作为指导信号，通过迭代去噪进一步去除兴趣向量中的噪声成分。为了增强去噪效果，进一步设计了目标引导的多兴趣损失，将推荐目标显式融入多兴趣学习过程，为各兴趣分配合理的责任度，在优化层面减弱兴趣特定噪声的干扰。在Book、Beauty和Retail Rocket三个真实世界数据集上的实验结果表明，相较于基线模型中最优的Top50推荐结果，所提方法的召回率（Recall）分别提升8.84%、2.03%、2.27%，命中率（HR）分别提升9.78%、0.95%、0.72%，归一化折损累计增益（NDCG）分别提升9.07%、3.87%和2.49%，上述实验结果验证了该方法的合理性和有效性。

Abstract: In recent years, an increasing number of studies have focused on modeling users’ multiple interests from their behavioral sequences in order to better capture complex user preferences. However, in implicit modeling scenarios where external auxiliary information such as item categories is unavailable, existing multi-interest models often struggle to accurately determine the interest attribution of individual behaviors. As a result, items that are weakly related or even irrelevant to the target interest are easily aggregated into the same interest representation, leading to the introduction of interest-specific noise. To address this issue, we propose a two-stage denoising multi-interest recommendation algorithm, termed DMIRec, which suppresses interest-specific noise at both the item-feature level and the interest-representation level. In the item denoising stage, learnable adaptive filters are employed to filter out irrelevant item features within each interest, yielding denoised behavior sequences for different interests. In the interest denoising stage, a conditional diffusion model is introduced, where items highly related to the current interest serve as guidance signals to iteratively remove noise components from the corresponding interest representations. Furthermore, to enhance the overall denoising effectiveness, we design a target-guided multi-interest loss that explicitly incorporates the recommendation target into the multi-interest learning process. This loss encourages appropriate responsibility assignment among different interests and reduces the influence of interest-specific noise from an optimization perspective. Experiments conducted on three real-world datasets, Book, Beauty, and Retail Rocket, show that, compared with the best Top-50 recommendation results among baseline models, the proposed method achieves improvements of 8.84%, 2.03%, and 2.27% in Recall; 9.78%, 0.95%, and 0.72% in Hit Rate (HR); and 9.07%, 3.87%, and 2.49% in Normalized Discounted Cumulative Gain (NDCG), respectively. These results demonstrate the effectiveness and robustness of the proposed approach.