Comments (9)
I think NCF addresses implicit feedback as well. For dataset only containing interactions between users and items, you can try to use BPR(Bayesian Personalized Ranking) criterion as the loss function. Specifically, the existing interactions are positive samples while the negative ones are sampled manually.
from neural-collaborative-filtering.
According to the origin paper, NCF is proposed to deal with implicit feedback. May I ask why this repo used normalization to process ratings?
from neural-collaborative-filtering.
Negative items get 0 ratings in this repo. And I normalized the ratings into [0, 1]. I think it is fine if you do not normalize the rating. But it might be hard to tune the hyper-params then.
from neural-collaborative-filtering.
Negative items get 0 ratings in this repo. And I normalized the ratings into [0, 1]. I think it is fine if you do not normalize the rating. But it might be hard to tune the hyper-params then.
I take a look at the implementation by the authors and found the function here. They just set all negative items to 0 and all other items interacted with users to 1 rather normalizing them (implicit feedback V.S. explicit feedback).
from neural-collaborative-filtering.
@RuihongQiu Thank you for reporting the bug. I added support for implicit feedback in the latest commit. Could you check if it works well? I only tested the GMF
.
from neural-collaborative-filtering.
@LaceyChen17 I will check it out soon.
Thanks a lot! I think the new code works.
I have checked all the experiments with new rating settings.
The first two experiments are actually explicit feedback with normalization on ratings.
Filenames ended with "implicit" are the result of the newest commit.
I also implement a new binarize method which just works as how "_normalize" works. It avoids the many lines change of codes compared to the newest version.
The results of binarize methods are filenames ended with "binarize".
If you would'n mind, I can pull a request.
from neural-collaborative-filtering.
Pull requests are extremely welcome!!! BTW Could you also share your training curves by updating README.md ?
from neural-collaborative-filtering.
I've opened a pull request for update.
from neural-collaborative-filtering.
Merged. Thank you so much!
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Related Issues (19)
- AssertionError: CUDA is not available HOT 4
- Add LICENSE.txt
- the performance of ncf in hr@10 didn't achieve 0.7 HOT 4
- NeuMF algorithm HOT 1
- L2 regularization HOT 1
- Missing layer and training workflow HOT 2
- Does hyper parameter "l2_regularization" works? I didn't find anywhere in "mlp.py" related to this parameter
- size mismatch for fc_layers
- ModuleNotFoundError: No module named 'tensorboardX'
- The NDCG metric HOT 3
- AssertionError
- The GPU utilization is low HOT 2
- The code implementation does not match the original paper HOT 4
- 'checkpoints/gmf_factor8neg4_Epoch100_HR0.6391_NDCG0.2852.model' HOT 12
- question about the version of pytorch HOT 8
- 0 NDCG and HR for training NeuMF and GMF HOT 2
- Test Dataloader for large dataset. HOT 4
- Wrong Neumf config ? HOT 1
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