Traceback (most recent call last):
File "main.py", line 126, in
prediction = model(features, feature_values)
File "C:\Users\qiwang\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "C:\Users\qiwang\Desktop\NFM-pyorch-master\model.py", line 83, in forward
nonzero_embed = self.embeddings(features)
File "C:\Users\qiwang\anaconda3\lib\site-packages\torch\nn\modules\module.py", line 493, in call
result = self.forward(*input, **kwargs)
File "C:\Users\qiwang\anaconda3\lib\site-packages\torch\nn\modules\sparse.py", line 117, in forward
self.norm_type, self.scale_grad_by_freq, self.sparse)
File "C:\Users\qiwang\anaconda3\lib\site-packages\torch\nn\functional.py", line 1506, in embedding
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but got CUDAType instead (while checking arguments for embedding)

when I run your original code, bug raised above, can you fix it?

this NFM implementation is really good, how can I turn this rating prediction method to a pair-wise ranking issue just like the BPR-torch repo u did before?
where should I change the code? loss function or other parts?

Thx.

Can u tell me what is the exact meaning of each column in ml-tag.train.libfm file?

Hi, guoyang9.
I am a new learner of deep learning. I want to refer to your research but I have a question about the dataste. The dataset you use in this program is libfm format I think. I find the original dataset is csv format. How do you change it? I do not the concept and method. Could you give me an instruction? Thank you very much!