[jira] [Commented] (MAHOUT-1089) SGD matrix factorization for rating prediction with user and item biases

["clem clem (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-1089?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14322615#comment-14322615 ] 

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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