| 摘 要： | Next location prediction has aroused great inter-ests in the era of internet of things(IoT).With the ubiquitous deployment of sensor devices,e.g..GPS and Wi-Fi,loT en-vironment offers new opportunities for proactively analyzing human mobility patterns and predicting user's future visit in low cost,no matter outdoor and indoor.In this paper,we con-sider the problem of next location prediction in loT environ-ment via a session-based manner.We suggest that user's future intention in each session can be better inferred for more ac-curate prediction if patterns hidden inside both trajectory and signal strength sequences ollected from IoT devices can be jointly modeled,which however existing state-of the-art meth-ods have rarely addressed.To this end,we propose a trajectory and sIgnal sequence(TSIS)model,where the trajectory transi-tion regularities and signal temporal dynamics are jointly embedded in a neural network based model.Specifically,we employ gated recurrent unit(GRU)for capturing the temporal dy-namics in the mutivariate signal strength sequence.Moreover,we adapt gated graph neural networks(gated GNNs)on loca-tion transition graphs to explicitly model the transition patterns of trajectories.Finally,both the low-dimensional representa-tions learned from trajectory and signal sequence are jointly optimized to construct a session embedding,which is further employed to predict the next location.Extensive experiments on two real-world Wi-Fi based mobility datasets demonstrate that TSIS is effective and robust for next location prediction pompared with other competitive baselines.
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