Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation'paper (IJCAI'18)
The implementation of twin networks in the '- 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
- 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
-
How to collect results from different seeds: Use collect_result.sh
-
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