DeepMove: Predicting Human Mobility with Attentional Recurrent Networks

@article{Feng2018DeepMovePH,
  title={DeepMove: Predicting Human Mobility with Attentional Recurrent Networks},
  author={J. Feng and Y. Li and Chao Zhang and F. Sun and Fanchao Meng and Ang Guo and Depeng Jin},
  journal={Proceedings of the 2018 World Wide Web Conference},
  year={2018}
}
  • J. Feng, Y. Li, +4 authors Depeng Jin
  • Published 2018
  • Computer Science
  • Proceedings of the 2018 World Wide Web Conference
Human mobility prediction is of great importance for a wide spectrum of location-based applications. [...] Key Method In DeepMove, we first design a multi-modal embedding recurrent neural network to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility.Expand
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References

SHOWING 1-6 OF 6 REFERENCES
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
  • 333
  • Highly Influential
  • PDF
Predicting future locations with hidden Markov models
  • 253
  • Highly Influential
  • PDF
Long Short-Term Memory
  • 37,265
  • Highly Influential
  • PDF
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
  • 5,505
  • Highly Influential
  • PDF
Supervised Sequence Labelling with Recurrent Neural Networks
  • A. Graves
  • Computer Science
  • Studies in Computational Intelligence
  • 2008
  • 1,469
  • Highly Influential
  • PDF
Neural Machine Translation by Jointly Learning to Align and Translate
  • 15,079
  • Highly Influential
  • PDF