Session-based recommendations aim to predict the next behavior of users based on ongoing sessions. The previous works have been modeling the session as a variable-length of a sequence of items and learning the representation of both individual items and the aggregated session. Recent research has applied graph neural networks with an attention mechanism to capture complicated item transitions and dependencies by modeling the sessions into graph-structured data. However, they still face fundamental challenges in terms of data and learning methodology such as sparse supervision signals and noisy interactions in sessions, leading to sub-optimal performance. In this paper, we propose SR-GCL, a novel contrastive learning framework for a session-based recommendation. As a crucial component of contrastive learning, we propose two global context enhanced data augmentation methods while maintaining the semantics of the original session. The extensive experiment results on two real-world E-commerce datasets demonstrate the superiority of SR-GCL as compared to other state-of-the-art methods.

该研究提出了一种新的对话式推荐框架SR-GCL，该框架基于对话图模型，采用对比学习和全局上下文增强数据增强方法来提升推荐性能，并在两个真实的电子商务数据集上进行了广泛的实验，表明相较于其他最先进的方法，SR-GCL具有卓越的性能。

SR-GCL: 具有全局上下文增强的对比学习中基于会话的推荐