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Modeling Contemporaneous Basket Sequences with Twin Networks for Next-Item Recommendation
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README.md
collect_result.sh
layers.py
main_gpu.py
main_gpu.sh
models.py
procedure.py
requirements.txt
utils.py

README.md

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

@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

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