ARNN

PyTorch implementation of AAAI-20 paper-ARNN: An Attentional Recurrent Neural Networkfor Personalized Next Location Recommendation. link

Note: I'm not among the authors of the paper ARNN. Without source code from the authors of ARNN, I can only reproduct this model based on DeepMove, which is a similar model. So if there are any errors, please contact me.

Datasets

The foursquare data to evaluate our model can be found in the data folder, which contains 1000+ users and is real-world datasets.

Requirements

• Python 3.6
• Pytorch 1.7.0
• cudatoolkit 9.2

Project Structure

• /codes
  • data_pre_with_category.py
  • generate_graph.py
  • main.py
  • model.py
  • train.py
  • utils.py
• /data
  • dataset_TSMC2014_NYC.txt
  • foursquare_NYC_4input.pkl
  • nyc_4input.pkl
  • paths_NYC.pkl
  • triple_pc.txt
  • triple_plg.txt
  • triple_ptp.txt
  • triple_utp.txt
  • relation_category_id.txt
  • relation_loc_id.txt
  • relation_td_id.txt
  • relation_time_id.txt
  • entity_category_name_id.txt
  • entity_grid_id.txt
  • entity_loc_dict.txt
  • entity_user_dict.txt
• /resutls
  • /checkpoint
    • ep_i.m

Usage

1. Prepare the session data and KG data:

python data_pre_with_category.py

In this part, we conduct the raw data to filter and form check-in sessions and spatial-temproal-category triples.

2. Discover neighbors:

python generate_graph.py

Using meta-path based random walk method to discover every location's plenty neighbors. You can choose the meta-path you need within the code. You can also determine the length of the paths.

And it may take some time to finish.

Output file: paths_NYC.pkl

2. Train a new model:

python main.py

The parameters are already set in the code.

Other parameters:

• 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, word_emb_size, hidden_size, neighbors_num, rnn_type, attn_type
  • history_mode: avg, avg, whole

Author

Haoyu Huang

E-mail: haoyuhuang@bjtu.edu.cn

References

The modle was implemented base on the codes of DeepMove.code link