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https://issues.apache.org/jira/browse/MAHOUT-586?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Lance Norskog updated MAHOUT-586:
---------------------------------
Fix Version/s: (was: 0.5)
0.6
Description:
The RecommenderEvaluator implementation is hard-coded around the standard
algorithm for comparing a recommender's performance on training v.s. test
datasets.
This is a rewrite supplying other algorithms, and the ability to directly
compare DataModels. It was born out of a desire to check that a new recommender
made sense compared to existing recommenders, and to directly work with
DataModels that encompass the entire recommendation space.
was:
The RecommenderEvaluator implementation is hard-coded around one algorithm.
This is a more flexible, modular rewrite.
I'm happy with this iteration. It is trimmed down to three implementations that
make sense:
* Precision (directly matching the research paper meaning)
* Mean Reciprocal Rank (creatively interpreted)
* Bubble sort (should be Ulam or Levenshtein distance)
And includes the ability to track the average or RMS of these comparisons.
Please reopen and commit this.
> Redo RecommenderEvaluator for modularity
> ----------------------------------------
>
> Key: MAHOUT-586
> URL: https://issues.apache.org/jira/browse/MAHOUT-586
> Project: Mahout
> Issue Type: Improvement
> Components: Collaborative Filtering
> Reporter: Lance Norskog
> Assignee: Sean Owen
> Fix For: 0.6
>
> Attachments: MAHOUT-559.patch, MAHOUT-586.patch, MAHOUT-586.patch,
> MAHOUT-586.patch, MAHOUT-586.patch, MAHOUT-586.patch
>
>
> The RecommenderEvaluator implementation is hard-coded around the standard
> algorithm for comparing a recommender's performance on training v.s. test
> datasets.
> This is a rewrite supplying other algorithms, and the ability to directly
> compare DataModels. It was born out of a desire to check that a new
> recommender made sense compared to existing recommenders, and to directly
> work with DataModels that encompass the entire recommendation space.
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