# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 1 Introduction
These graphs are used in the entire training process without any optimization. However, these artificially created graphs may lack accuracy, contain noise, or miss key connections, ultimately leading to poorer performance. (3) In order to accurately recommend POIs to users, the model needs to consider other factors, such as temporal and spatial factors in addition to the sequential patterns. Some works [17; 18] choose to separately consider sequential and spatial factors, successfully capturing users' geographical preference, but are likely to recommend POIs in the neighborhood. It remains a critical factor to capture the connections between different check-ins and potentially influential factors. To address the aforementioned limitation (1), this paper employs a heterogeneous graph neural network to model the heterogeneous relations between users and POIs. When considering heterogeneous graphs, most studies only consider whether there is an interaction relation between users and POIs, often overlooking the potential high-order connections between them. Additionally, not all datasets contain sufficient information, particularly about users. The lack of user information can hinder the construction of a graph that accurately reflects user relations. To tackle these issues, we introduce a method using meta-paths as the solution. We firstly use the provided information in the datasets to construct a heterogeneous graph, including users, POIs, and POI attributes. As a valid tool for analyzing heterogeneous graphs, meta-paths can help identify potential connections between users and POIs. For instance, the meta-path UPU represents two users who have visited the same POI. By using different meta-paths, we can capture various types of connections between users and POIs, thereby constructing different meta-path-based subgraphs. To enhance the quality of the representation vectors, contrastive learning is applied to promote the representation learning of the heterogeneous graph and the meta-path subgraphs. Specifically, it designs two kinds of contrastive learning methods based on different meta-paths to learn the representation vectors of users and POIs. To address the aforementioned limitation (2), we utilize graph structure learning to continuously refine the artificially constructed graph. The artificially constructed graph may contain noise and is not always complete. To tackle this problem, researchers have introduced graph structure learning, which aims to learn a more optimal graph structure though the training pipeline rather than directly using the original one without any modification. Most graph structure learning methods use a hybrid approach, where both the graph structure and GNN parameters are updated in conjunction with downstream tasks. In this study, the meta-path subgraphs used to learn the representations of users and POIs are artificially constructed graphs. Directly utilizing these graphs might result in inaccurate connections between nodes. Therefore, this paper employs graph structure learning to continuously optimize the structure of these meta-path subgraphs, thereby achieving more accurate relations between nodes. To address the aforementioned limitation (3), this paper employs graph neural networks to model users' preferences in check-in sequences. To capture the connections between different check-ins, we first convert each check-in sequence into a sequence graph, and then use graph neural networks to learn these connections, treating the POIs visited by users as nodes and encoding the users' sequence graphs using a graph encoder based on the attention mechanism. Given that users' check-in behaviors are influenced by multiple factors, this paper incorporates time, space, and sequence into the computation of the graph convolutional neural network, thereby better generating the users' preference representations. Based on the above analysis, we propose a model named Heterogeneous Graph Structure Learning for Next POI Recommendation (HGSL-POI).