This is the official source code of "BiCAT: Bi-Chronological Augmentation of Transformer for Sequential Recommendation".

Last update: Dec 06, 2022

Overview

BiCAT

This is our TensorFlow implementation for the paper: "BiCAT: Sequential Recommendation with Bidirectional Chronological Augmentation of Transformer". Our code is implemented based on Tensorflow version of SASRec and ASReP.

Environment

TensorFlow 1.12
Python 3.6.*

Datasets Prepare

Benchmarks: Amazon Review datasets Beauty, Movie Lens and Cell_Phones_and_Accessories. The data split is done in the leave-one-out setting. Make sure you download the datasets from the link. Please, use the DataProcessing.py under the data/, and make sure you change the DATASET variable value to your dataset name, then you run:

python DataProcessing.py

You will find the processed dataset in the directory with the name of your input dataset.

Beauty

1. Reversely Pre-training and Short Sequence Augmentation

Pre-train the model and output 20 items for sequences with length <= 20.

python main.py \
       --dataset=Beauty \
       --train_dir=default \
       --lr=0.001 \
       --hidden_units=128 \
       --maxlen=100 \
       --dropout_rate=0.7 \
       --num_blocks=2 \
       --l2_emb=0.0 \
       --num_heads=4 \
       --evalnegsample 100 \
       --reversed 1 \
       --reversed_gen_num 20 \
       --M 20

2. Next-Item Prediction with Reversed-Pre-Trained Model and Augmented dataset

python main.py \
       --dataset=Beauty \
       --train_dir=default \
       --lr=0.001 \
       --hidden_units=128 \
       --maxlen=100 \
       --dropout_rate=0.7 \
       --num_blocks=2 \
       --l2_emb=0.0 \
       --num_heads=4 \
       --evalnegsample 100 \
       --reversed_pretrain 1 \
       --aug_traindata 15 \
       --M 18

Cell_Phones_and_Accessories

1. Reversely Pre-training and Short Sequence Augmentation

Pre-train the model and output 20 items for sequences with length <= 20.

python main.py \
       --dataset=Cell_Phones_and_Accessories \
       --train_dir=default \
       --lr=0.001 \
       --hidden_units=32 \
       --maxlen=100 \
       --dropout_rate=0.5 \
       --num_blocks=2 \
       --l2_emb=0.0 \
       --num_heads=2 \
       --evalnegsample 100 \
       --reversed 1 \
       --reversed_gen_num 20 \
       --M 20

2. Next-Item Prediction with Reversed-Pre-Trained Model and Augmented dataset

python main.py \
       --dataset=Cell_Phones_and_Accessories \
       --train_dir=default \
       --lr=0.001 \
       --hidden_units=32 \
       --maxlen=100 \
       --dropout_rate=0.5 \
       --num_blocks=2 \
       --l2_emb=0.0 \
       --num_heads=2 \
       --evalnegsample 100 \
       --reversed_pretrain 1 \ 
       --aug_traindata 17 \
       --M 18

Citation

@misc{jiang2021sequential,
      title={Sequential Recommendation with Bidirectional Chronological Augmentation of Transformer}, 
      author={Juyong Jiang and Yingtao Luo and Jae Boum Kim and Kai Zhang and Sunghun Kim},
      year={2021},
      eprint={2112.06460},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

This is the official source code of "BiCAT: Bi-Chronological Augmentation of Transformer for Sequential Recommendation".

Related tags

Overview

BiCAT

Environment

Datasets Prepare

Beauty

Cell_Phones_and_Accessories

Citation

Owner

John

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