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hi,余老师,在看您的代码的时候,我发现和LightGCN的代码是有些不同的。
l2_reg_loss的设置,LightGCN中是使用了0层embedding去计算reg_loss的 去实验了一下,即使不使用较小的学习率,也能够提升到和论文接近的指标。
LightGCN-torch版本中,计算bpr loss时使用的负采样也是不一样的,如下图
因为我自己在跑LightGCN的时候遇到了复现不出来LightGCN结果的问题,也看到有其他人在问这个问题,所以请您看看会不会有以上的原因。
The text was updated successfully, but these errors were encountered:
第一个应该是有影响的 你可以试一下
负采样所有算法都一样的 应该问题不大
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非常感谢您的回复!
我测试的结果是,
不同的reg_loss计算方法在LightGCN上会有影响,使用LightGCN原文中用0层embedding计算reg_loss可以复现出来LightGCN的结果;在SimGCL上没有影响,SimGCL依然保持了论文中的性能。
负采样算法影响会有一些影响,在不同的模型上表现不同,具体哪个好也无法下定论,所以采用一样的负采样是可以的。
好的 你可以提交一个PR fix一下LightGCN
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hi,余老师,在看您的代码的时候,我发现和LightGCN的代码是有些不同的。
l2_reg_loss的设置,LightGCN中是使用了0层embedding去计算reg_loss的
![image](https://private-user-images.githubusercontent.com/58901253/340990417-dad3b977-0850-428d-bc77-49250e76ec62.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.aTZJWKUMaLtNhjMacLCAL4ULtIXhrH7ZOmVkJfNLyEU)
![image](https://private-user-images.githubusercontent.com/58901253/340990827-8315421a-f7ba-4878-a3dc-f9f38cff3d2d.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.Xtsw6imrt1DYZt6gcqh9A_86rPL4JxMrlY2bmhXGHIo)
去实验了一下,即使不使用较小的学习率,也能够提升到和论文接近的指标。
LightGCN-torch版本中,计算bpr loss时使用的负采样也是不一样的,如下图
![image](https://private-user-images.githubusercontent.com/58901253/340992059-7e2a932f-aaea-4f24-87b0-3262bb92f534.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.1WDEI1I5qNkLU4RzOWeH_Ru0aQqSMSRwk7n8ENR9HqQ)
因为我自己在跑LightGCN的时候遇到了复现不出来LightGCN结果的问题,也看到有其他人在问这个问题,所以请您看看会不会有以上的原因。
The text was updated successfully, but these errors were encountered: