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The implementation of "Correlation-Sensitive Next-Basket Recommendation"", published in IJCAI'19
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README.md
cmatrix_generator.py
layers.py
main_gpu.py
models.py
procedure.py
utils.py

README.md

The implementation of Beacon in the "Correlation-Sensitive Next-Basket Recommendation" paper (IJCAI'19)

  1. Input format(s): Train/Validate/Test sets have the same format
  • For each basket sequence, baskets {b_i} are separated by '|', e.g., b_1|b_2|b_3|...|b_n

  • For each basket b_i, items {v_j} are separated by a space ' ', e.g., v_1 v_2 v_3 ... v_m

  1. How to train:
  • Step 1: Generate pre-computed correlation matrix C using cmatrix_generator.py.
    • The 'nbhop' parameter is to generate the Nth-order correlation matrix
    • The default output directory is "data_dir/adj_matrix"
  • Step 2: Train the Beacon model using main_gpu.py ".
    • Support 3 modes: train_mode, prediction_mode, tune_mode
    • The format of the prediction file is as follows: Target:gt_basket|item_candidate_1:score_1|item_candidate_2:score_2|
  1. If you find the code useful in your research, please cite:
@inproceedings{le2019beacon,
  title={Correlation-Sensitive Next-Basket Recommendation},
  author={Le, Duc-Trong, Lauw, Hady W and Fang, Yuan},
  booktitle={Proceedings of the International Joint Conference on Artificial Intelligence},
  year={2019},
}

Requirements

  • Python == 3.6
  • Tensorflow == 1.14
  • scipy.sparse == 1.3.0

@Please drop me an email (ductrong.le.2014 at smu.edu.sg) if you need any clarification. Thanks 👍

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