ASReP

This is our TensorFlow implementation for the paper:

Zhiwei Liu*, Ziwei Fan*, Yu Wang and Philip S. Yu (2021). Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformer in SIGIR 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