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The implementation for SIGIR 2025: Bridging Short Videos and Streamers with Multi-Graph Contrastive Learning for Live Streaming Recommendation.

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MGCCDR

The implementation for SIGIR 2025: Bridging Short Videos and Streamers with Multi-Graph Contrastive Learning for Live Streaming Recommendation.

How to run the code

To train MGCCDR on Doubanbook dataset with GPU 0, simply run:

python train.py -g 0 -m MGCCDR -d Doubanbook

Environment

Our experimental environment is shown below:

numpy version: 1.24.4
pandas version: 2.0.3
torch version: 2.4.1

Citation

If you find our code or work useful for your research, please cite our work.

@inproceedings{qu2025bridging,
  title={Bridging Short Videos and Streamers with Multi-Graph Contrastive Learning for Live Streaming Recommendation},
  author={Qu, Changle and Zhao, Liqin and Niu, Yanan and Zhang, Xiao and Xu, Jun},
  booktitle={Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages={2059--2069},
  year={2025}
}

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The implementation for SIGIR 2025: Bridging Short Videos and Streamers with Multi-Graph Contrastive Learning for Live Streaming Recommendation.

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