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I'm trying to improve the mahout recommendation implementation in a project, and I found out that my predecessor used tanimotoCoefficientSimilarity for a dataset with preference value 1-5. I changed it to UncenteredCosineSimilarity, and now I'm trying to test its improvement in performance.

I tried using AverageAbsoluteDifferenceEvaluator on both, but realised that this should not be used for Tanimoto since it does not return the expected value of the preference.

However, the value seems odd and I don't quite understand what the value this implementation is returning represents. The average preference value of the dataset is 3.2, and if Tanimoto was to return a value in the range [0,1], then the output of AverageAbsoluteDifferenceEvaluator must be in the range [2.2, 3.2], but it consistently returns a value in the range [0.8, 1.1].

Does anyone have explanation for this?

Thank you.

zoonoo
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1 Answers1

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TanimotoCoefficientSimilarity works without coefficients - so AverageAbsoluteDifferenceEvaluator not have any sense for TanimotoCoefficientSimilarity

Goko Gorgiovski
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