This is the implementation for the paper: BigData'22. You may find it on Arxiv
The code is built on Pytorch. The dataset preprocess code is in data/ dir. Change the path in the code and also include the meta data file path.
Code to run:
python main.py --data_name=Beauty --lr=0.001 --hidden_size=128 --output_dir=relationsasrec_v6/ --max_seq_length=100 --hidden_dropout_prob=0.3 --num_hidden_layers=1 --weight_decay=0.0 --num_attention_heads=1 --model_name=RelationAwareSASRecModel --attention_probs_dropout_prob=0.1 --rel_loss_weigh=0.1 --outseq_rel_loss_weight=0.05
Please cite our paper if you use the code:
@inproceedings{fan2022sequentialmt4sr,
title={Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformer},
author={Fan, Ziwei and Liu, Zhiwei and Wang, Chen and Huang, Peijie and Peng, Hao and Philip, S Yu},
booktitle={2022 IEEE International Conference on Big Data (Big Data)},
pages={525--534},
year={2022},
organization={IEEE}
}