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xiangwang1223 / knowledge_graph_attention_network

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
MIT License
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Best hyper parameters in your paper section 4.2.3 #19

Open rugezhao opened 4 years ago

rugezhao commented 4 years ago

Hi, I am trying to reproduce the results in your paper, but I could not find the best hyper-parameters in the paper or repo. Can you share more information on hyperparameters for each dataset?

xiangwang1223 commented 4 years ago

Thanks for your interest. Please get the latest version from github. For the parameter settings, please refer to the README file; for the corresponding training log, please refer to the log files. Thanks.

rugezhao commented 4 years ago

Both the README and https://github.com/xiangwang1223/knowledge_graph_attention_network/blob/master/Log/training_log_amazon-book.log are using the pretrained embeddings. I'm wondering what the parameters are used for training from scratch.

srtianxia commented 4 years ago

@rugezhao @xiangwang1223 I think this code is different from the paper such as the loss of KGE, In paper, the loss of KGE is contain Wr, but not in code (in code, Wr is used to calculate attention) ... I am more confused about this approach

xiangwang1223 commented 4 years ago

Please CAREFULLY check the lines 194-199 in KGAT.py, where the model parameters "trans_W" are used to calculate the KGE loss, which is CONSISTENT to Equation (1) in the paper; and check the line 395, where the same parameters "trans_W" are used to calculate the attention scores, which is also CONSISTENT to Equation (4) in the paper.

ALL the codes are the same as the formulation in the paper.

srtianxia commented 4 years ago

Please CAREFULLY check the lines 194-199 in KGAT.py, where the model parameters "trans_W" are used to calculate the KGE loss, which is CONSISTENT to Equation (1) in the paper; and check the line 395, where the same parameters "trans_W" are used to calculate the attention scores, which is also CONSISTENT to Equation (4) in the paper.

ALL the codes are the same as the formulation in the paper.

Thanks for your reply！but the loss of KGE is in lines 229-252, Wr is really not in the loss of KGE, If I made a mistake, please point out, thank you! https://github.com/xiangwang1223/knowledge_graph_attention_network/blob/6eb71fc0b63370484bd71bbf9eb3ceaf6270559f/Model/KGAT.py#L229-L252

srtianxia commented 4 years ago

I found my mistake, thank you for your correction @xiangwang1223