Comments (3)
hey @itsoum, yes that would be great if you can undertake adding the new metrics. Feel free to email if you need any support. Thanks!
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this is great find. I reviewed both sources and Item similarity is already covered by recmetrics "personalization" metrics. However, Discounted Cumulative Gain (DCG) and normalized Discounted Cumulative Gain (nDCG/NDCG) is likely worth adding to recmetrics. I'll take it down as a feature request.
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@statisticianinstilettos I am using last 6 months Discounted Cumulative Gain (DCG) and normalized Discounted Cumulative Gain (nDCG/NDCG) for evaluation in a problem which looks as a combination of a typical multi-label classification but with order of labels to be important (ranking problem). Here is a nice explanation of them with also some other usefull metrics: https://amitness.com/2020/08/information-retrieval-evaluation/
I am thinking undertake this enhancement issue given that I know the structure of your project because I have commit some metrics in the past and I don't say no to another Hacktoberefest t-shirt :)
Is it ok?
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Related Issues (20)
- ImportError: cannot import name 'signature' HOT 8
- Slows down even if x_labels=False
- module 'recmetrics' has no attribute 'prediction_coverage' HOT 2
- ImportError: cannot import name 'signature'
- Metric: familiarity HOT 1
- Unable to import recmetrics HOT 3
- Is this library actively maintained HOT 1
- Unused Requirement HOT 2
- Installation issues HOT 7
- Personalization metric calculation optimization HOT 1
- personalization() has explosive memory requirements due to pairwise comparison HOT 3
- Coverage over 100% HOT 2
- Integration with Deep Learning Based Frameworks
- Catalog coverage is changed when using the exact same input arguments
- Implement MAP@k HOT 4
- dev dependencies breaking installation
- module 'recmetrics' has no attribute 'mapk'
- any method for AP@k or AP@K calculation? HOT 1
- mapk shouldn't require actual and predicted have the same length
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