ilya-shenbin / recvae Goto Github PK

Hi, nice work about Variational Autoencoder on recommendation. However, I am confused about the method of data split which is the same way as 2018WWW-Variational autoencoders for collaborative filtering
In the

Looking forward to your reply, Thanks.
Best.

Greetings to the team!
Your code is beautiful, but I'm lost in the implementation of it.

I have the last version of PyTorch: https://pytorch.org/get-started/locally/ & Python

• Stable (1.8.1)
• OS: Windows
• Package: PiP
• Computer Platform: CUDA 11.1
• Python: 3.8.7

When I train the model I receive the outputs:

• test_te.csv
• test_tr.csv
• validation_tr.csv
• validation_te.csv
• train
• unique_sid
• unique_uid
  (For k = 100 in utils.py)

If I change the utils.py in the return of ndcg function for: return DCG/IDCG to: return np.divide(DCG,IDCG, where=IDCG!=0) it solve it, but still giving 'low' values. The latter in comparison with other VAE-CF results.

Can you help me, please?

Congratulations. I reiterate, your code is beautiful in comparison to tensorflow-keras implementation.

Best regards,
Mirko.

i have dataset: train.csv 5869632 number, user:53424,item:10000; then run error;
RuntimeError: CUDA out of memory. Tried to allocate 13.12 GiB (GPU 0; 23.70 GiB total capacity; 0 bytes already allocated; 12.33 GiB free; 0 bytes reserved in total by PyTorch)