## 1 Introduction  
Point-of-Interest (POI) recommendation is a critical service in location-based social networks (LBSNs) and has garnered significant attention in the field of recommender systems [1, 2, 3]. Unlike traditional recommendation domains such as movies or music, capturing user preferences in POI recommendation poses unique challenges. These challenges are primarily manifested in two key aspects: (1) User's behavior is influenced by multiple contextual factors. In POI recommendation, user's check-in behavior is affected by both temporal and geographical factors. For instance, users typically check into dining POIs at restaurants and are unlikely to visit POIs that are too far away; (2) User historical records are highly sparse. POI recommendation relies on check-in records, which are generated only when a user physically visits a location. This leads to a significantly smaller volume of user activity data compared to other recommendation domains. The sparsity of user check-in data exacerbates the difficulty in accurately modeling user preferences and predicting future check-in behaviors. Based on the aforementioned challenges, traditional techniques, such as collaborative filtering methods, are ineffective for POI recommendation. Recently, researchers have attempted to incorporate graph-based models into POI recommendation to enhance model performance. The key idea behind these graph-based approaches [4; 5] is to transform user check-in records into graph-structured data, thereby leveraging the rich relational information inherent in graphs. In this way, these methods aim to explore the relationships between users and POIs within multiple contextual information, such as temporal and geographical factors. This allows for more accurate modeling of user preference characteristics and enhances the overall performance of the recommendation system. Graph-based POI recommendation models can be broadly categorized into two main phases: graph construction and graph learning. In the graph construction phase, the primary objective is to structure the data in a way that preserves the impact of contextual features. This involves encoding the relationships between POIs within various contextual information, such as temporal and geographical factors. The constructed graph captures the interactions among users, POIs, and their associated contexts, forming a rich relational structure that serves as the foundation for subsequent learning processes. The graph learning phase focuses on building models that can effectively learn the representations of both POIs and users. These models are typically based on graph neural networks (GNNs). The modeling process involves feeding the constructed graph structural data into a GNN-based architecture to extract meaningful representations of users and POIs. However, models based on graph neural networks (GNNs) face inherent limitations in their message-passing mechanisms, such as over-smoothing and over-squashing issues. These limitations hinder the effective extraction of POI and user representations from graph-structured data with multiple contextual features, ultimately constraining the performance of GNN-based models in POI recommendation tasks. To address the aforementioned challenges, we introduce a novel model named Multi-view Contextual Graphs via Convolutional Neural Networks for Point-of-Interest Recommendation (MCGRec). This framework consists of three main components: (1) Super node-based PPR Sampling. This component employs a personalized PageRank (PPR) method based on super nodes to sample highly correlated POIs from the context-feature-based graph-structured data. The sampled POIs are then transformed into a grid-like feature matrix, which serves as input for the subsequent convolutional neural network (CNN) processing; (2) Convolutional neural network-based backbone. A CNN-based model is utilized to extract POI representations from the grid-like feature matrix.