Session-based recommendation (SBR) systems aim to utilize the user's short-term behavior sequence to predict the next item without the detailed user profile. Most recent works try to model the user preference by treating the sessions as between-item transition graphs and utilize various graph neural networks (GNNs) to encode the representations of pair-wise relations among items and their neighbors. Some of the existing GNN-based models mainly focus on aggregating information from the view of spatial graph structure, which ignores the temporal relations within neighbors of an item during message passing and the information loss results in a sub-optimal problem. Other works embrace this challenge by incorporating additional temporal information but lack sufficient interaction between the spatial and temporal patterns. To address this issue, inspired by the uniformity and alignment properties of contrastive learning techniques, we propose a novel framework called Session-based Recommendation with Spatio-Temporal Contrastive Learning Enhanced GNNs (RESTC). The idea is to supplement the GNN-based main supervised recommendation task with the temporal representation via an auxiliary cross-view contrastive learning mechanism. Furthermore, a novel global collaborative filtering graph (CFG) embedding is leveraged to enhance the spatial view in the main task. Extensive experiments demonstrate the significant performance of RESTC compared with the state-of-the-art baselines e.g., with an improvement as much as 27.08% gain on HR@20 and 20.10% gain on MRR@20.

翻译：基于会议的建议系统(SBR)旨在利用用户的短期行为序列来预测下一个没有详细用户配置的下一个项目。最近的一些工作试图模拟用户的偏好,将会议视为项目过渡图,并使用各种图形神经网络(GNNS)来编码各项目及其邻居之间双向关系的表达方式。一些基于GNN的现有模型主要侧重于从空间图形结构的角度汇总信息,这种模式忽视了信息传递过程中某项目邻居之间的时间关系,信息丢失结果在一个亚最佳问题中出现。其他工作通过纳入额外的时间信息来迎接这一挑战,但在空间和时间模式之间缺乏足够的互动。为了解决这一问题,在对比性学习技术的统一和一致性特性的启发下,我们提出了一个名为会议建议的新框架,与Spatio-动态兼容学习增强GNNNNS(RES)的强化GTC(RES)系统。其想法是通过一个辅助的交叉对比学习机制来补充基于GNN20的主要监督建议任务的时间代表方式。此外,一个新的全球合作过滤图表(CFGN8)将MRCR(C)与M(M-R)的大幅增长定位定位定位定位定位作为空间定位的模型,将提升的模型作为空间定位的模型的模型的模型,以提升的模型作为模型的模型的模型的模型的模型作为基础,将提升的模型作为基础。在20RLBLBLMT(M-L)的模型的模型的模型的模型的模型的模型的嵌入为模型,以大大的定位的模型,以提升的定位的定位的定位的定位的定位的模型,将提升的模型作为空间定位的定位的定位的定位的定位的模型的模型的定位的定位的定位的模型的模型的模型的模型的模型的模型的模型作为模型的模型的模型作为模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型的模型。