# Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation

arXiv: https://arxiv.org/abs/2209.11461 · HTML: https://mlsanity.com/paper/2209.11461/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2022.

## Built on by (most cited first, 8 of 8)

- [Med-UniC: Unifying Cross-Lingual Medical Vision-Language Pre-Training by Diminishing Bias](https://mlsanity.com/paper/2305.19894/) (2023)
- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [D2O: Dynamic Discriminative Operations for Efficient Long-Context Inference of Large Language Models](https://mlsanity.com/paper/2406.13035/) (2024)
- [Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns](https://mlsanity.com/paper/2312.16199/) (2023)
- [Self-consistent Reasoning For Solving Math Word Problems](https://mlsanity.com/paper/2210.15373/) (2022)
- [Towards Pattern-aware Data Augmentation for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2501.00252/) (2024)
- [Session-based Recommender Systems: User Interest as a Stochastic Process in the Latent Space](https://mlsanity.com/paper/2504.10005/) (2025)
- [A Dataset for Spatiotemporal-Sensitive POI Question Answering](https://mlsanity.com/paper/2505.10928/) (2025)

## Abstract (arXiv)

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.