Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!

Dingqi Yang · Benjamin Fankhauser · Paolo Rosso · Philippe Cudré-Mauroux

197 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

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Human Mobility and Location-Based Analysis Data Management and Algorithms Traffic Prediction and Management Techniques

Abstract

Location prediction is a key problem in human mobility modeling, which predicts a user's next location based on historical user mobility traces. As a sequential prediction problem by nature, it has been recently studied using Recurrent Neural Networks (RNNs). Due to the sparsity of user mobility traces, existing techniques strive to improve RNNs by considering spatiotemporal contexts. The most adopted scheme is to incorporate spatiotemporal factors into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme oversimplifies the temporal periodicity and spatial regularity of user mobility, and thus cannot fully benefit from rich historical spatiotemporal contexts encoded in user mobility traces. Against this background, we propose Flashback, a general RNN architecture designed for modeling sparse user mobility traces by doing flashbacks on hidden states in RNNs. Specifically, Flashback explicitly uses spatiotemporal contexts to search past hidden states with high predictive power (i.e., historical hidden states sharing similar contexts as the current one) for location prediction, which can then directly benefit from rich spatiotemporal contexts. Our extensive evaluation compares Flashback against a sizable collection of state-of-the-art techniques on two real-world LBSN datasets. Results show that Flashback consistently and significantly outperforms state-of-the-art RNNs involving spatiotemporal factors by 15.9% to 27.6% in the next location prediction task.

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References

2016Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts 2014Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs 2009Understanding individual human mobility patterns 2012Predicting future locations with hidden Markov models 2012Mining User Mobility Features for Next Place Prediction in Location-Based Services 2019Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach 2021Exploring Social-Historical Ties on Location-Based Social Networks

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2021Self-supervised human mobility learning for next location prediction and trajectory classification 2021MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction 2021A Survey on Deep Learning for Human Mobility 2024Human mobility prediction with causal and spatial-constrained multi-task network 2023Predicting Human Mobility via Self-Supervised Disentanglement Learning 2023A Multi-Context Aware Human Mobility Prediction Model Based on Motif-Preserving Travel Preference Learning

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