The official implementation of "DGCN: Diversified Recommendation with Graph Convolutional Networks" (WWW '21)

Related tags

Recommender Systems diversity recommender-system recommendation diversification graph-neural-networks
Overview

DGCN

This is the official implementation of our WWW'21 paper:

Yu Zheng, Chen Gao, Liang Chen, Depeng Jin, Yong Li, DGCN: Diversified Recommendation with Graph Convolutional Networks, In Proceedings of the Web Conference 2021.

First unzip the datasets and start the visdom server:

visdom -port 33333

Then simply run the following command to reproduce the experiments on corresponding dataset and model:

python app.py --flagfile ./config/xxx.cfg

If you use our codes and datasets in your research, please cite:

@inproceedings{zheng2021dgcn,
  title={DGCN: Diversified Recommendation with Graph Convolutional Networks},
  author={Zheng, Yu and Gao, Chen and Chen, Liang and Jin, Depeng and Li, Yong},
  booktitle={Proceedings of the Web Conference 2021},
  pages={401--412},
  year={2021}
}
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Comments
  • Running Issues

    Running Issues

    Hi. I got this issue when I run the code. "Please turn it into an immutable graph with DGLGraphStale.readonly" AssertionError: NeighborSampler doesn't support mutable graphs. Please turn it into an immutable graph with DGLGraphStale.readonly

    Seems it got the error at File "recommender.py", line 161, in inference data_flows, node_index, user_index, item_index = self.gm.point_sample_neighbors(user, item).

    Do you have ideas? BTW, could you also please provide the version of the DGL and pytorch? Mant thanks

    opened by Shawn617 1
Owner
FIB LAB, Tsinghua University
FIB LAB, Tsinghua University
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