The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"
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Try running it python2. That worked for me.
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The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"
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The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"
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The official PyTorch implementation of the paper "RecVAE: A New Variational Autoencoder for Top-N Recommendations with Implicit Feedback"
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issue openedilya-shenbin/RecVAE
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.
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