ASReP

This is our TensorFlow implementation for the paper: SIGIR'21 ([PDF](coming soon)

@inproceedings{liu2021augmenting,
  title={Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer},
  author={Liu, Zhiwei* and Fan, Ziwei* and Wang, Yu and Yu, Philip S.},
  booktitle={Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year={2021}
}

The code is implemented based on Tensorflow version of SASRec. Please cite our paper if you use the code.

Environment Setup

The code is tested under a Linux desktop (w/ GTX 1080 Ti GPU) with TensorFlow 1.12 and Python 3.6. Create the requirement with the requirements.txt

Datasets

We use the Amazon Review datasets Beauty 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.

Data Preprocessing

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.

Baby Dataset Pre-training and Prediction

Reversed Pre-train 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

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 Dataset Pre-training and Prediction

Reversed Pre-train 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

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