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https://issues.apache.org/jira/browse/MAHOUT-1089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14326961#comment-14326961
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Jesse Daniels commented on MAHOUT-1089:
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I haven't studied the code in depth but I have seen tricks like this used to
avoid having to create separate vectors to store the biases. Essentially these
"factors" get a value of 1 and when the two factor matrices are multiplied it
reveals the biases. Not exactly sure about the third value but it's probably
something similar. Do the factor vectors have more elements than the number of
factors you specified? If so then the remaining values are likely bias values.
> 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
> Affects Versions: 0.7
> Reporter: Zeno Gantner
> Assignee: Sebastian Schelter
> Fix For: 0.8
>
> Attachments: MAHOUT-1089.patch, RatingSGDFactorizer.java,
> 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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