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Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation

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The implementation of twin networks in the 'Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation'paper (IJCAI'18)

  1. Input format(s):
  • For each CBS instance, the basket sequences and the grouth-truth item are separated by '=>'
    • e.g., support_basket_sequence=>target_basket_sequence=>ground-truth_item_id
  • 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 run: main_gpu.sh
  • Use --train_mode to enable the training mode
  • Use --prediction_mode to generate evaluation metrics
  • We support 5 main model types namely: bseq_support, bseq_target, cbs_sn, cbs_cfn, cbs_dfn
  1. How to collect results from different seeds: Use collect_result.sh

  2. If you find the code useful in your research, please cite:

@inproceedings{le2018cbs,
  title={Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation},
  author={Le, Duc-Trong, Lauw, Hady W and Fang, Yuan},
  booktitle={Proceedings of the International Joint Conference on Artificial Intelligence},
  year={2018},
}

Requirements

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

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Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation

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