Model input: CSR vs COO sparse matrices ? (Different results)
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Description
I noticed a strange issue using the library with the sparse matrices format.
If I use the same interaction matrix but as CSR while training, I get different results when evaluating compared to using a COO matrix for interactions.
Is it a normal behavior ?
Also how can I be certain of the format I should use for all LighFM input:
lightfm.Dataset returns all COO matrices when building interactions and features, but in the source it's said that user_features and item_features should be as CSR.
Thanks for you help !
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Research direction
Start by reproducing the reported comparison using the same interaction matrix in CSR and COO formats, then compare the training and evaluation results. Check how LightFM handles the formats returned by lightfm.Dataset and determine whether the differing results indicate a bug or need clarification in the input-format guidance.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100