Sequential recommender systems (SRSs) aim to suggest next item for a user based on her historical interaction sequences. Recently, many research efforts have been devoted to attenuate the influence of noisy items in sequences by either assigning them with lower attention weights or discarding them directly. The major limitation of these methods is that the former would still prone to overfit noisy items while the latter may overlook informative items. To the end, in this paper, we propose a novel model named Multi-level Sequence Denoising with Cross-signal Contrastive Learning (MSDCCL) for sequential recommendation. To be specific, we first introduce a target-aware user interest extractor to simultaneously capture users' long and short term interest with the guidance of target items. Then, we develop a multi-level sequence denoising module to alleviate the impact of noisy items by employing both soft and hard signal denoising strategies. Additionally, we extend existing curriculum learning by simulating the learning pattern of human beings. It is worth noting that our proposed model can be seamlessly integrated with a majority of existing recommendation models and significantly boost their effectiveness. Experimental studies on five public datasets are conducted and the results demonstrate that the proposed MSDCCL is superior to the state-of-the-art baselines. The source code is publicly available at https://github.com/lalunex/MSDCCL/tree/main.

翻译：序列推荐系统旨在根据用户的历史交互序列为其推荐下一个项目。近年来，许多研究工作致力于通过降低噪声项目的注意力权重或直接丢弃它们来减弱序列中噪声项目的影响。这些方法的主要局限性在于：前者仍容易过度拟合噪声项目，而后者可能忽略信息性项目。为此，本文提出一种名为多层级序列降噪与跨信号对比学习（MSDCCL）的新型序列推荐模型。具体而言，我们首先引入目标感知用户兴趣提取器，在目标项目的引导下同时捕捉用户的长期与短期兴趣；其次，开发多层级序列降噪模块，通过软硬信号降噪策略减轻噪声项目的影响；此外，我们通过模拟人类学习模式扩展了现有课程学习机制。值得注意的是，本模型可与大多数现有推荐模型无缝集成，并显著提升其效果。在五个公开数据集上的实验结果表明，所提出的MSDCCL优于现有最先进基线方法。源代码已公开于https://github.com/lalunex/MSDCCL/tree/main。