Sequential recommendation aims to predict the next item based on user interests in historical interaction sequences. Historical interaction sequences often contain irrelevant noisy items, which significantly hinders the performance of recommendation systems. Existing research employs unsupervised methods that indirectly identify item-granularity irrelevant noise by predicting the ground truth item. Since these methods lack explicit noise labels, they are prone to misidentify users' interested items as noise. Additionally, while these methods focus on removing item-granularity noise driven by the ground truth item, they overlook interest-granularity noise, limiting their ability to perform broader denoising based on user interests. To address these issues, we propose Multi-Granularity Sequence Denoising with Weakly Supervised Signal for Sequential Recommendation(MGSD-WSS). MGSD-WSS first introduces the Multiple Gaussian Kernel Perceptron module to map the original and enhance sequence into a common representation space and utilizes weakly supervised signals to accurately identify noisy items in the historical interaction sequence. Subsequently, it employs the item-granularity denoising module with noise-weighted contrastive learning to obtain denoised item representations. Then, it extracts target interest representations from the ground truth item and applies noise-weighted contrastive learning to obtain denoised interest representations. Finally, based on the denoised item and interest representations, MGSD-WSS predicts the next item. Extensive experiments on five datasets demonstrate that the proposed method significantly outperforms state-of-the-art sequence recommendation and denoising models. Our code is available at https://github.com/lalunex/MGSD-WSS.

翻译：序列推荐旨在基于用户在历史交互序列中的兴趣预测下一个物品。历史交互序列常包含无关的噪声物品，这显著阻碍了推荐系统的性能。现有研究采用无监督方法，通过预测真实物品间接识别物品粒度的无关噪声。由于这些方法缺乏明确的噪声标签，容易将用户感兴趣的物品误判为噪声。此外，尽管这些方法专注于移除由真实物品驱动的物品粒度噪声，却忽视了兴趣粒度噪声，限制了其基于用户兴趣进行更广泛去噪的能力。为解决这些问题，我们提出基于弱监督信号的多粒度序列去噪用于序列推荐（MGSD-WSS）。MGSD-WSS首先引入多高斯核感知器模块，将原始序列与增强序列映射到共同表示空间，并利用弱监督信号准确识别历史交互序列中的噪声物品。随后，它采用带有噪声加权对比学习的物品粒度去噪模块，获得去噪后的物品表示。接着，从真实物品中提取目标兴趣表示，并应用噪声加权对比学习获得去噪后的兴趣表示。最后，基于去噪后的物品与兴趣表示，MGSD-WSS预测下一个物品。在五个数据集上的大量实验表明，所提方法显著优于当前最先进的序列推荐与去噪模型。我们的代码公开于https://github.com/lalunex/MGSD-WSS。