Comments (2)
I have also found that the calculation of precision@k is incorrect. When trying to find out what was wrong, I noticed that the p@k results were identical to the results I got when calculating the recall@k myself, so I think the implicit library's p@k may be returning the recall@k instead of the precision.
from implicit.
I have also found that the calculation of precision@k is incorrect. When trying to find out what was wrong, I noticed that the p@k results were identical to the results I got when calculating the recall@k myself, so I think the implicit library's p@k may be returning the recall@k instead of the precision.
Its because of fmin(K, likes.size())
. If the average number of interactions per user in the test is lower enough than k, the denominator in line 471 often be the same as in the recall formula, so in this case the result of such precision realization is identical to the recall
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Related Issues (20)
- Evaluation module not working HOT 1
- Adding `numpy` to `requirements.txt`
- pip install implicit is not work to use GPU HOT 3
- Incremental update with old users but new items HOT 4
- als explain method bug HOT 1
- Usage of the trained model on testing.
- ranking metrics fail when model trained on CPU but not GPU?; implicit 0.7.2 HOT 1
- Python issue HOT 4
- Is it possible to compare scores across multiple results?
- function get_lastfm() error
- getting index error while using recommend function while ALS algorithm
- What is a good enough Precision@K and MAP@K value?
- BPR results not reproducible using multiple threads
- gpu issue on colab (implicit==0.7.2 / cuda ==12.1) HOT 1
- Early stopping for ALS
- How recommend method works in Nearest Neighbour Models? HOT 3
- Potential bug in calculation of gradient updates for BPR
- IMPLICIT: No CUDA extension has been built, can't train on GPU HOT 1
- Cuda Error: an illegal memory access was encountered. When latent factors = 1024.
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