Hi yoongi0428,

Thanks a lot for sharing this extensive recommender system implementation. I have tried to run your code on python 3.8, however I run into trouble with the cython backend. I get an error of the form:

/local/home/scheidlf/.pyxbld/temp.linux-x86_64-3.8/pyrex/utils/backend/cython/tool.c:610:10: fatal error: numpy/arrayobject.h: No such file or directory #include "numpy/arrayobject.h" ^~~~~~~~~~~~~~~~~~~~~ compilation terminated.

I was wondering if you could share the environment you worked with or let us know which packages are required to run the code.

Best, Flo

Hi,

First of all, thanks for sharing this strong work! I found this repo would be really useful for researchers.

While the overall code structure is very clean and intuitive to me, I have some questions about the evaluation code.

1. I was just wondering what before_evaluation methods(in Model classes) are.

2. I was wondering if your evaluation code is based on the assumption that the shape of the training matrix and test matrix are guaranteed to be the same or not. I found that the numbers of users in training and test matrices might be different as some users are dropped from the matrix in df_to_sparse. Does the evaluation code work even those shapes are not matched?

3. Is there any reference about the input normalization for DAE(CDAE)?

  # normalize
  user_degree = torch.norm(rating_matrix, 2, 1).view(-1, 1)   # user, 1
  item_degree = torch.norm(rating_matrix, 2, 0).view(1, -1)   # 1, item
  normalize = torch.sqrt(user_degree @ item_degree)
  zero_mask = normalize == 0
  normalize = torch.masked_fill(normalize, zero_mask.bool(), 1e-10)

  normalized_rating_matrix = rating_matrix / normalize

Thanks!