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Codes for WWW'18 Paper-DeepMove: Predicting Human Mobility with Attentional Recurrent Network
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codes update readme and delete setproctitle dependency Jun 16, 2018
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README.md

DeepMove

PyTorch implementation of WWW'18 paper-DeepMove: Predicting Human Mobility with Attentional Recurrent Networks link

Datasets

The sample data to evaluate our model can be found in the data folder, which contains 800+ users and ready for directly used. The raw mobility data similar to ours used in the paper can be found in this public link.

Requirements

cPickle is used in the project to store the preprocessed data and parameters. While appearing some warnings, pytorch 0.3.0 can also be used.

Project Structure

  • /codes
  • /pretrain
    • /simple
      • res.m # pretrained model file
      • res.rs # detailed evaluation results
      • res.txt # evaluation results
    • /simple_long
    • /attn_local_long
    • /attn_avg_long_user
  • /data # preprocessed foursquare sample data (pickle file)
  • /docs # paper and presentation file
  • /resutls # the default save path when training the model

Usage

  1. Load a pretrained model:
python main.py --model_mode=attn_avg_long_user --pretrain=1

The codes contain four network model (simple, simple_long, attn_avg_long_user, attn_local_long) and a baseline model (Markov). The parameter settings for these model can refer to their res.txt file.

model_in_code model_in_paper top-1 accuracy (pre-trained)
markov markov 0.082
simple RNN-short 0.096
simple_long RNN-long 0.118
attn_avg_long_user Ours attn-1 0.133
attn_local_long Ours attn-2 0.145
  1. Train a new model:
python main.py --model_mode=attn_avg_long_user --pretrain=0

Other parameters (refer to main.py):

  • for training:
    • learning_rate, lr_step, lr_decay, L2, clip, epoch_max, dropout_p
  • model definition:
    • loc_emb_size, uid_emb_size, tim_emb_size, hidden_size, rnn_type, attn_type
    • history_mode: avg, avg, whole

Others

Batch version for this project will come soon.

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