Comments (2)
evfro
commented on May 27, 2024
@dbalabka hi, thanks for the report. I'd say that it really depends on the data, as I have observed a vice versa effects in some cases. But I totally agree that it should be configurable. I have updated the LightFM wrapper with 2 new attributes for normalization:
You can set them just normally as any other attribute on a wrapper instance of lightfm.
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dbalabka
commented on May 27, 2024
@evfro thanks for response. It was a while after I have created this ticket. Our recommendations system already implemented using original LightFM code and we run another phase of AB testing. So, this issue became less relevant. Initliay we had an idea to use polara, but decided to use LightFM directly.
We are going to perform more experiments. I suspect that normalization issue related to one-hot encoding approach. There are some discussions in Scikit Learn repository about this topic
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Related Issues (14)
- Python 3 compatibility HOT 2
- ModuleNotFoundError: No module named 'polara.recommender.hybrid' HOT 1
- Get recommendation from PureSVD model given user's preference HOT 8
- Scaled svd and hybrid svd is giving non deterministic output HOT 5
- polara.preprocessing module not found HOT 5
- ModuleNotFoundError: No module named 'polara' HOT 1
- Notebook 'Comparing LightFM with HybridSVD.ipynb' HOT 3
- NotImplementedError: Data has duplicate values HOT 2
- Precision@K HOT 1
- Conda and Pip package
- ModuleNotFoundError: No module named 'polara.recommender' HOT 5
- TypeError: __init__() got an unexpected keyword argument 'item_features' HOT 3
- TypeError: 'int' object is not iterable HOT 5
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