This is a copy project of Beacon in the "Correlation-Sensitive Next-Basket Recommendation" paper (IJCAI'19)
- 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
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For each basket b_i, items {v_j} are separated by a space ' ', e.g., v_1 v_2 v_3 ... v_m
- 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|
- 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},
}
- Python == 3.6
- Tensorflow == 1.14
- scipy.sparse == 1.3.0
@Please drop an email (ductrong.le.2014 at smu.edu.sg) if you need any clarification. Thanks 👍