Causal Incremental Graph Convolution for Recommender System Retraining

@article{Ding2021CausalIG,
  title={Causal Incremental Graph Convolution for Recommender System Retraining},
  author={Sihao Ding and Fuli Feng and Xiangnan He and Yong Liao and Jun Shi and Yongdong Zhang},
  journal={IEEE transactions on neural networks and learning systems},
  year={2021},
  volume={PP}
}
The real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN)-based recommender models that are state-of-the-art techniques for the collaborative recommendation. To pursue high efficiency, we set the target as using only new data for model updating, meanwhile not sacrificing the recommendation accuracy compared with full model retraining. This is nontrivial to achieve since the… 

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