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https://issues.apache.org/jira/browse/MAHOUT-1089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14322615#comment-14322615
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clem clem commented on MAHOUT-1089:
-----------------------------------
Hi,
I have been using the SGD matrix factorization for the Yelp Business dataset. I
called the factorize() method and saved the output which is a matrix of
doubles. Now I am trying to understand the actual meaning of the values I
obtained (I suspect the category of the business is one of the latent factors,
and the gender of the users could be another one). But when I call the
getItemFeatures(), for each item the first two values are always equal to 1.0.
In the same way, when I call the getUserFeatures(), for each user the third
value is always equal to 1.0.
If anybody has the time to explain this to me I would be really grateful.
> 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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