An Attention-Based Spatiotemporal GGNN for Next POI Recommendation

• Published in: IEEE access Vol. 10; pp. 26471 - 26480
• Main Authors: Li, Quan, Xu, Xinhua, Liu, Xinghong, Chen, Qi
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: attention; Context modeling; cross entropy; Embedding; gated graph neural network; Graph neural networks; Graphical representations; Logic gates; Neural networks; Nodes; POI recommendation; Recurrent neural networks; Social networking (online); Spatiotemporal phenomena; Task analysis; window pooling
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3156618

The task of Point-of-Interest (POI) recommendation is to recommend the next interest locations for users. Gated Graph Neural Network (GGNN) has been proved to be effective on POI recommendation tasks. However, existing GGNN solutions rarely consider the spatiotemporal information between nodes in the sequence graph, which is essential for modeling user check-in behaviors in next POI recommendation. In this paper, we propose an attention-based spatiotemporal gated graph neural network model (ATST-GGNN) for next POI recommendation. Firstly, the user's check-in sequence is represented as a graph structure. Secondly, we use spatiotemporal context information to dynamically update nodes in the sequence graph, and obtain the complex transfer relationships between the check-ins. Thirdly, each session is then represented as the composition of the long and short preference using an attention network. However, current short preference fails to model union-level sequential patterns, we improve the local embedding representation of graph nodes by window pooling method, as well as the global embedding representation of graph nodes by integrating it into attention mechanism. Finally, the objective function is constructed by cross entropy and the model parameters are learned. The experimental results show that the precision rate and mean reciprocal ranking of ATST-GGNN method are greatly improved compared with the state-of-art methods. It has good application prospect.