# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
###### Abstract  
As one of the most crucial topics in the recommendation system field, point-of-interest (POI) recommendation aims to recommending potential interesting POIs to users. Recently, graph neural networks have been successfully used to model interaction and spatio-temporal information in POI recommendations, but the data sparsity of POI recommendations affects the training of GNNs. Although some existing GNN-based POI recommendation approaches try to use social relationships or user attributes to alleviate the data sparsity problem, such auxiliary information is not always available for privacy reasons. Self-supervised learning provides a new idea to alleviate the data sparsity problem, but most existing self-supervised recommendation methods are designed for bi-partite graphs or social graphs, and cannot be directly used in the spatio-temporal graph of POI recommendations. In this paper, we propose a new method named SSTGL to combine self-supervised learning and GNN-based POI recommendation for the first time. SSTGL is empowered with spatio-temporal-aware strategies in the data augmentation and pre-text task stages, respectively, so that it can provide high-quality supervision information by incorporating spatio-temporal prior knowledge. By combining self-supervised learning objective with recommendation objectives, SSTGL can improve the performance of GNN-based POI recommendations. Extensive experiments on three POI recommendation datasets demonstrate the effectiveness of SSTGL, which performed better than existing mainstream methods. POI recommendation; graph neural network; self-supervised learning +
Footnote †: journal:  
Since GNN-based models usually rely on topology for message passing, the sparsity of interactions means that many nodes (especially long-tail nodes) cannot learn high-quality representations and are susceptible to interaction noise. To mitigate the impact of data sparsity on GNNs, some works have introduced various auxiliary information, including social relations [10] and user attributes [11]. However, as user privacy is taken more seriously these days, such auxiliary information is not always available. Therefore, there is an urgent need to explore new methods to reduce the impact of data sparsity problems on GNN-based approaches. The recent emergence of self-supervised learning techniques offers a new direction to this problem. Through data augmentation and design pretext tasks, self-supervised learning can provide additional supervision signals to improve the performance of recommendation algorithms. In fact, some recent works have attempted to combine self-supervised learning with GNNs for other recommendation tasks (e.g., product recommendation [12; 13], social recommendation [14], and session-based recommendation [15]). For example, SGL [12] designed three graph structure-based data augmentation methods, and NCL [13] designed graph structure-based and semantic prototype-based pretext tasks. However, these methods are specialized for bi-partite graphs, social graphs, or session-based hypergraphs, and do not consider spatio-temporal information. Thus, there is still a gap to adopt self-supervised learning for POI recommendation algorithms based on spatio-temporal graphs. In this paper, we explore for the first time how self-supervised learning can be applied to GNN-based POI recommendations and design a general framework named self-supervised spatio-temporal graph learning (SSTGL). Specifically, SSTGL combines spatio-temporal prior knowledge in two ways, i.e., via data augmentation and the pre-text task. We first define the temporal and spatial similarity of POIs based on the interaction time between POIs and users as well as the geographical location of POIs. Then, considering that users may be interested in POIs that are similar to the interacted POIs, SSTGL adds some implicit edges to users and POIs based on the similarity function between POIs, which implements spatial or temporal-based data augmentation.