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Posted to dev@mahout.apache.org by "Sebastian Schelter (Created) (JIRA)" <ji...@apache.org> on 2011/11/08 09:18:51 UTC
[jira] [Created] (MAHOUT-877) Enable the parallel ALS recommender
to use implicit feedback data
Enable the parallel ALS recommender to use implicit feedback data
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Key: MAHOUT-877
URL: https://issues.apache.org/jira/browse/MAHOUT-877
Project: Mahout
Issue Type: New Feature
Components: Collaborative Filtering
Affects Versions: 0.6
Reporter: Sebastian Schelter
Assignee: Sebastian Schelter
Mahout's ParallelALSFactorizationJob offers a distributed matrix factorization for computing recommendations. The current implementation is only suited for explicit feedback data (ratings) unfortunately.
The majority of usecases has to work with implicit feedback however. The paper "Collaborative Filtering for Implicit Feedback Datasets" http://research.yahoo.com/pub/2433 describes a closely related approach that is aimed at implicit feedback data and should easily be integratable into the current ParallelALSJob.
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[jira] [Commented] (MAHOUT-877) Enable the parallel ALS recommender
to use implicit feedback data
Posted by "Hudson (Commented) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-877?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13146309#comment-13146309 ]
Hudson commented on MAHOUT-877:
-------------------------------
Integrated in Mahout-Quality #1161 (See [https://builds.apache.org/job/Mahout-Quality/1161/])
MAHOUT-877 Enable the parallel ALS recommender to use implicit feedback data
ssc : http://svn.apache.org/viewcvs.cgi/?root=Apache-SVN&view=rev&rev=1199171
Files :
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/hadoop/als/ParallelALSFactorizationJob.java
* /mahout/trunk/core/src/main/java/org/apache/mahout/cf/taste/impl/recommender/svd/ALSWRFactorizer.java
* /mahout/trunk/core/src/test/java/org/apache/mahout/cf/taste/hadoop/als/ParallelALSFactorizationJobTest.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/als/AlternateLeastSquaresSolver.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/als/AlternatingLeastSquaresSolver.java
* /mahout/trunk/math/src/main/java/org/apache/mahout/math/als/ImplicitFeedbackAlternatingLeastSquaresSolver.java
* /mahout/trunk/math/src/test/java/org/apache/mahout/math/als/AlternateLeastSquaresSolverTest.java
* /mahout/trunk/math/src/test/java/org/apache/mahout/math/als/AlternatingLeastSquaresSolverTest.java
> Enable the parallel ALS recommender to use implicit feedback data
> -----------------------------------------------------------------
>
> Key: MAHOUT-877
> URL: https://issues.apache.org/jira/browse/MAHOUT-877
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Affects Versions: 0.6
> Reporter: Sebastian Schelter
> Assignee: Sebastian Schelter
> Fix For: 0.6
>
> Attachments: MAHOUT-877.patch
>
>
> Mahout's ParallelALSFactorizationJob offers a distributed matrix factorization for computing recommendations. The current implementation is only suited for explicit feedback data (ratings) unfortunately.
> The majority of usecases has to work with implicit feedback however. The paper "Collaborative Filtering for Implicit Feedback Datasets" http://research.yahoo.com/pub/2433 describes a closely related approach that is aimed at implicit feedback data and should easily be integratable into the current ParallelALSJob.
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[jira] [Resolved] (MAHOUT-877) Enable the parallel ALS recommender
to use implicit feedback data
Posted by "Sebastian Schelter (Resolved) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-877?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sebastian Schelter resolved MAHOUT-877.
---------------------------------------
Resolution: Fixed
Fix Version/s: 0.6
> Enable the parallel ALS recommender to use implicit feedback data
> -----------------------------------------------------------------
>
> Key: MAHOUT-877
> URL: https://issues.apache.org/jira/browse/MAHOUT-877
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Affects Versions: 0.6
> Reporter: Sebastian Schelter
> Assignee: Sebastian Schelter
> Fix For: 0.6
>
> Attachments: MAHOUT-877.patch
>
>
> Mahout's ParallelALSFactorizationJob offers a distributed matrix factorization for computing recommendations. The current implementation is only suited for explicit feedback data (ratings) unfortunately.
> The majority of usecases has to work with implicit feedback however. The paper "Collaborative Filtering for Implicit Feedback Datasets" http://research.yahoo.com/pub/2433 describes a closely related approach that is aimed at implicit feedback data and should easily be integratable into the current ParallelALSJob.
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[jira] [Updated] (MAHOUT-877) Enable the parallel ALS recommender
to use implicit feedback data
Posted by "Sebastian Schelter (Updated) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/MAHOUT-877?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sebastian Schelter updated MAHOUT-877:
--------------------------------------
Attachment: MAHOUT-877.patch
> Enable the parallel ALS recommender to use implicit feedback data
> -----------------------------------------------------------------
>
> Key: MAHOUT-877
> URL: https://issues.apache.org/jira/browse/MAHOUT-877
> Project: Mahout
> Issue Type: New Feature
> Components: Collaborative Filtering
> Affects Versions: 0.6
> Reporter: Sebastian Schelter
> Assignee: Sebastian Schelter
> Attachments: MAHOUT-877.patch
>
>
> Mahout's ParallelALSFactorizationJob offers a distributed matrix factorization for computing recommendations. The current implementation is only suited for explicit feedback data (ratings) unfortunately.
> The majority of usecases has to work with implicit feedback however. The paper "Collaborative Filtering for Implicit Feedback Datasets" http://research.yahoo.com/pub/2433 describes a closely related approach that is aimed at implicit feedback data and should easily be integratable into the current ParallelALSJob.
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