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[CIKM 2024] Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation

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Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation

Pytorch Implementation for CIKM 2024 Full Research Track Paper:

Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation. 33rd ACM International Conference on Information and Knowledge , CIKM 2024
Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Liancheng Fang, Philip S. Yu

Investigation of Role of Graph Convolution BFS (GCN) vs DFS (MF) perspective The Embedding Discrepancy Issue

Framework

Set up:

Dependencies

pip install -r requirements.txt

Dataset Preparation

Get the beauty, toys-and-games, gowalla dataset under dataset folder Overall file structure:

LightGODE/
    ├─ Dataset/
        ├─ amazon-beauty
        ├─ amazon-toys-games
        ├─ gowalla

Running on different datasets:

Amazon-Beauty

python run_recbole.py -m LightGODE -d amazon-beauty

Amazon-Toys-and-Games

python run_recbole.py -m LightGODE -d amazon-toys-games

Gowalla

python run_recbole.py -m LightGODE -d gowalla -w 0

Acknowledgement

The structure of this repo is built based on RecBole. Thanks for their great work.

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[CIKM 2024] Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation

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