A Fair Benchmark for GNN-based Recommendation
| Algo | nDCG@5 | nDCG@10 | nDCG@15 |
|---|---|---|---|
| MF | 0.158707 | 0.196456 | 0.218138 |
| Ours-MF | 0.166521 | 0.206430 | 0.230114 |
| Algo | nDCG@5 | nDCG@10 | nDCG@15 |
|---|---|---|---|
| MF | 0.087726 | 0.096501 | 0.104771 |
| Ours-MF | 0.099239 | 0.108716 | 0.118175 |
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Hi, Thanks for making the code public! I have a single question about hyperparameters tuning.. In the original paper both NGCF and LightGCN, the authors mentioned that they split the data via train/valid/test. But there are no validation data in the author's Github code. Also, I could not find any validation data in your code.
So, here is the question. How do you conduct hyperparameters tuning without any validation datasets? If you use a test dataset to tune hyperparameters, I think that is unfair. If you do not agree with this, please let me know. Best regards
recommendationsystem matrixfactorization colloborative filtering recommendation
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