Point-of-Interest Recommendation Model Based on Graph Convolutional Neural Network

With the development of location-based social networks, the point-of-interest recommendation has become one of the research hotspots in the field of recommendation. However, traditional technologies like collaborative filtering are limited by the influence of data sparsity and cannot accurately capture the users' preferences from the complex context. In order to address this problem, a recommendation model based on graph convolutional neural network is proposed, named RMGCN. RMGCN is composed of three parts: graph structure features extraction module, geographical factor evaluation module, and score calculation module. The graph structure feature extraction module is used to extract node features from the graph structure data composed of user check-in records. The geographical factor evaluation module is used to calculate the influence coefficient of geographical factors on user's decision-making behaviors. The score calculation module is used to combine the output results of the above two modules and calculate the user's preference scores of point-of-interests combined with temporal context and spatial context. Experimental results on two real-world datasets show that RMGCN has better recommendation performance than baselines.