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https://issues.apache.org/jira/browse/MAHOUT-1089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13486309#comment-13486309
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Zeno Gantner commented on MAHOUT-1089:
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w/o learning rate decay, good parameter combinations for ml-1m were
preventOverfitting=0.05 numFeatures=5 learningRate=0.005 iterations=75 (RMSE
0.8745)
and
0.075 50 0.01 75 (RMSE: 0.8725)
I tried
numFeatures 5, 10, 20, 30, 50
preventOverfitting 0.05, 0.075, 0.1, 0.15, 0.2
learnRate 0.0025, 0.005, 0.01, 0.025, 0.05, 0.075
once on an 80/20 split -- so take those numbers with a grain of salt.
> SGD matrix factorization for rating prediction with user and item biases
> ------------------------------------------------------------------------
>
> Key: MAHOUT-1089
> URL: https://issues.apache.org/jira/browse/MAHOUT-1089
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Reporter: Zeno Gantner
> Assignee: Sebastian Schelter
> Attachments: MAHOUT-1089.patch, RatingSGDFactorizer.java
>
>
> A matrix factorization that is trained with standard SGD on all features at
> the same time, in contrast to ExpectationMaximizationFactorizer, which learns
> feature by feature.
> Additionally to the free features it models a rating bias for each user and
> item.
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