Comments (2)
The initial factors are randomly assigned - and the output will be different every time afterwards (though the overall loss should converge to the same value).
To remove variability - the unittests for that function seed the RNG with a fixed value: https://github.com/benfred/implicit/blob/master/tests/als_test.py#L65 .
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Ok, thanks
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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
- Potential error in the calculation of precision@k HOT 2
- 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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