# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 1 Introduction  
As Internet technology advances and mobile devices become more prevalent, an increasing number of users are willing to share location information and experiences on their social media accounts. Location-based social network (LBSN) service providers offer a platform for these users to share. By analyzing the vast amount of data generated by users, these providers can recommend points-of-interest (POIs) that users may find interesting. This not only enhances user experience and convenience but also boosts revenue for service providers. To accurately predict users' future visits, it is essential that we capture users' preferences based on their historical behaviors. Early methods primarily relied on manually constructed feature extraction and traditional machine-learning techniques [1, 2, 3, 4]. However, due to the shortcomings in feature engineering, which need domain expertise, and the great challenge of big data, deep-learning methods have replaced most of the methods, with the ability to automatically extract various features [5, 6, 7, 8, 9]. In recent years, an increasing numberof studies have employed graph neural network models to capture global connections between POIs [10; 11; 12; 13]. These models typically consist of two stages: (1) constructing a graph based on the features of POIs, such as using the geographical distance between two POIs or the order of consecutive POIs visited by users; and (2) continuously updating the node representation vectors through message passing mechanisms. However, due to lack of data labels, these models may not produce high-quality representation vectors. In order to improve the quality of representation vectors, some models have adopted contrastive learning, which shows significant advantages in unsupervised representation learning. As a self-supervised learning method, it captures the internal connections within the data by constructing positive and negative samples, thereby mitigating the impact of data sparsity [14]. Graph contrastive learning, which combines graph neural networks with contrastive learning, has been proven to enhance the robustness of representation vectors in graphs. In recommendation systems, the existing research constructs different graphs on both sides of users and POIs to assist in the interaction graph between users and POIs, so as to learn the representations of users and POIs. However, although these studies have improved the performance of recommendation systems to some extent, they also have certain limitations: (1) Most studies are based on homogeneous relations between nodes, overlooking the heterogeneous relations in real life, which is more common. The homogeneous relation implies that the interactions belong to the same type or role, like the relation between two POIs, while the heterogeneous relation involves multiple types of roles or connections. It is conspicuous that heterogeneous relations are more ubiquitous in the real world. If we transform the heterogeneous relations into heterogeneous graphs, we can represent different relations between different kinds of nodes, like the interaction between users and POIs. Merely considering homogeneous relations may oversimplified the multi-role system. Although some research papers have considered heterogeneous relations, most only focus on the interaction between users and POIs, neglecting the potential connections between them. Additionally, not all datasets provide sufficient user information, which limits our ability to construct user views for contrastive learning from existing datasets. For example, the widely used datasets Foursquare-NYC and TKY do not include users' attributes or relations, limiting the construction of users' view for contrastive learning. (2) The graphs used in most studies are constructed based on predefined rules and remain unchanged during training. For example, [15] designs a graph capturing users' transition patterns and [16] figureproposes to use hypergraph to capture high-order relations.