Revisiting Self-attention for Cross-domain Sequential Recommendation

Source code for the paper Revisiting Self-attention for Cross-domain Sequential Recommendation accepted at KDD 2025.

by Clark Mingxuan Ju, Leonardo Neves, Bhuvesh Kumar, Liam Collins, Tong Zhao, Yuwei Qiu, Qing Dou, Sohail Nizam, Sen Yang, and Neil Shah.

The paper proposes AutoCDSR which utilizes vanilla self-attention transformers to achieve good performance for cross-domain sequential recommendation.

1. Installation

Please install all dependencies using the command:

conda create --name <env> --file requirements.txt

2. Run experiments

This is one example of reproducing results for KuaiRand-1K.

To train AutoCDSR+ model with BERT4Rec, run:

python src/train.py trainer=ddp experiment=kuairand model=hf_transformer_cd_sid_ib_kuairand_pareto

To train BERT4Rec model, run:

python src/train.py trainer=ddp experiment=kuairand model=hf_transformer_kuairand

3. Reference

If you find this repo and our work useful to you, please kindly cite us using:

@article{ju2025revisiting,
  title={Revisiting Self-attention for Cross-domain Sequential Recommendation},
  author={Ju, Clark Mingxuan and Neves, Leonardo and Kumar, Bhuvesh and Collins, Liam and Zhao, Tong and Qiu, Yuwei and Dou, Ching and Nizam, Sohail and Yang, Sen and Shah, Neil},
  journal={Proceedings of the 31st ACM SIGKDD conference on knowledge discovery and data mining},
  year={2025}
}

4. Contact

Please contact mju@snap.com for any questions.