CABIN: A Novel Cooperative Attention Based Location Prediction Network Using Internal-External Trajectory Dependencies

Abstract

Nowadays, large quantities of advanced locating sensors have been widely used, which makes it possible to deploy location-based service (LBS) enhanced by intelligent technologies. Location prediction, as one of the most fundamental technologies, aims to acquire possible location at next timestamp based on the moving pattern of current trajectories. High accuracy of location prediction could enrich and increase user experience of various LBSs and brings lots of benefits to service providers. Lots of state-of-the-art research try to model spatial-temporal trajectories based on recurrent neural networks (RNNs), yet fails to arrive at a practical usability. We observe that there exists two ways to improve through attention mechanism which performs well in computer vision and natural language processing domains. Firstly recent location prediction methods are usually equipped with single-head attention mechanism to promote accuracy, which is only able to capture limited information in a specific subspace at a specific position. Secondly, existing methods focus on external relations between spatial-temporal trajectories, but miss internal relations in each spatial-temporal trajectory. To tackle the problem of model spatial-temporal patterns of mobility, we propose a novel Cooperative Attention Based location prediction network using Internal-External trajectory dependencies correspondingly in this paper. We also design and perform experiments on two real-world check-in datasets, Foursquare data in New York and Tokyo cities. Evaluation results demonstrate that our method outperforms state-of-the-art models.