Session-based recommendation systems(SBRS) are more suitable for the current e-commerce and streaming media recommendation scenarios and thus have become a hot topic. The data encountered by SBRS is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel multi-granularity CL framework. Specifically, two extra augmented embedding convolution channels with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data(e.g. factor-level embeddings), with which we can construct factor-level convolution channels. At item-level, the star graph is deployed as the augmented data and graph convolution on it can ensure the effectiveness of self-supervised signals. Compare the learned embeddings of these two views with the learned embeddings of the basic view to achieve CL at two granularities. Finally, the more precise item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods.

翻译：会话推荐系统（SBRS）更适用于当前电子商务和流媒体推荐场景，因此成为研究热点。SBRS处理的数据通常高度稀疏，这也是限制推荐精度的主要瓶颈之一。对比学习（CL）因其在稀疏数据条件下能够提升嵌入学习能力而被应用于SBRS。然而，现有对比学习策略在实现更细粒度（如因子级别）比较方面存在局限，无法捕捉实例间的细微差异。此外，这些策略通常采用物品或会话片段的随机丢弃作为数据增强方法，可能导致数据更加稀疏，从而产生无效的自监督信号。针对上述两个限制，我们提出了一种新颖的多粒度对比学习框架。具体而言，我们构建了两种不同粒度的增强嵌入卷积通道，并将这些通道学习到的嵌入与原始视角的嵌入进行比较，以完成对比学习任务。在因子级别，我们采用解耦表示学习获取更细粒度的数据（如因子级嵌入），并以此构建因子级卷积通道。在物品级别，我们采用星型图作为增强数据，并对其进行图卷积以确保自监督信号的有效性。将这两个视角学习到的嵌入与基础视角学习到的嵌入进行比较，实现两个粒度的对比学习。最后，利用获得的更精确的物品级和因子级嵌入为用户生成个性化推荐。通过在两个基准数据集上进行的大量实验，验证了所提模型的优越性能，其表现优于现有方法。