Graph-based POI Recommendation through Self-Supervised Curriculum Learning

Point-of-Interest (POI) recommendation aims to enhance user experiences by offering tailored suggestions for places that align with their interests. Existing approaches to POI recommendation suffer from the data sparsity problem and fail to capture the temporal dependency among users’ check-in history. In this paper, we propose a novel POI recommendation approach based on graph neural networks. We encode each user’s historical check-ins using a graph structure, which preserves the temporal order of the users’ history and employs a graph neural network to learn users’ preferences. In addition, we apply self-supervised learning to facilitate data-efficient learning under the sparse setting and describe a curriculum-learning-based training strategy for POI recommendation. Experiments on two real-world, large-scale benchmark datasets demonstrate that the proposed model has strong performance, outperforming a number of state-of-the-art baseline alternatives by a wide margin. An extensive ablation study further verifies the effectiveness of each of the introduced modules.