  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.
