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：会话推荐系统旨在利用用户的短期行为序列预测下一个交互项目，无需依赖详细的用户画像。现有研究多将会话建模为项目间转移图，并利用各类图神经网络编码项目及其邻域项目之间的成对关系表示。部分基于图神经网络的模型主要从空间图结构视角聚合信息，忽略了消息传递过程中项目邻域内的时间关系，信息损失导致次优问题。另一些工作通过引入额外时间信息应对这一挑战，但缺乏时空模式的充分交互。为解决该问题，受对比学习技术的一致性与对齐特性启发，我们提出一个新型框架——基于时空对比学习增强图神经网络的会话推荐。其核心思想是通过辅助跨视图对比学习机制，用时间表征补充基于图神经网络的主监督推荐任务。此外，本文还利用一种新型全局协同过滤图嵌入增强主任务的空间视图。大量实验表明，与最先进基线相比，RESTC模型取得显著性能提升，例如在HR@20指标上最高提升27.08%，在MRR@20指标上最高提升20.10%。