HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

By Dejiang Kong, Fei Wu. International Joint Conference on Artificial Intelligence, 2018

314 citations recorded by the source corpus.

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a1bfad98d6192b3fad42054b07ff8a5acc2a97c9

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Citations in the corpus

Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “In such a case, RNN and LSTM models can be applied [37], [57], [70], [80], [126], [154], [156].”
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation (2019, methodology): “Moreover, STGCN consistently outperforms ﬁve RNN-based methods: RNN, LSTM, GRU, ST-RNN, and HST-LSTM.”
A Survey on Deep Learning for Human Mobility (2020, unknown): “HST-LSTM (Hierarchical Spatial-Temporal LSTM) [99] aims at predicting an individual’s short-term nextlocation.”