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https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15516142#comment-15516142
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ASF GitHub Bot commented on FLINK-4613:
---------------------------------------
GitHub user gaborhermann opened a pull request:
https://github.com/apache/flink/pull/2542
[FLINK-4613] Extend ALS to handle implicit feedback datasets
This extension of the ALS algorithm changes some parts of the code if
`implicitPrefs` flag is set to true. Mainly the local parts parts are changed:
the `Xt * X` computation takes into consideration the confidence, thus
computing `Xt * (C - I) * X` instead (see the paper by Hu et al. for details).
The `Xt * X` matrix is precomputed and broadcasted, and that is the only thing
that affects distributed execution.
Note, that we use a temporary directory in the test, because there would
not be enough memory segments to perform a hash join for prediction. I assume
that memory segments are not freed up after the training if no temporary
directory is set, but I did not investigate the issue as using a tempdir is a
simple workaround.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/gaborhermann/flink ials
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/flink/pull/2542.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #2542
----
commit 84d338b11f77b20fa1825029f8ca847a40eb4673
Author: Gábor Hermann <c...@gaborhermann.com>
Date: 2016-09-12T09:47:40Z
[FLINK-4613] Compute XtX for IALS & test, docs
commit 8e7c0d67a6f0390f03765fcdc9e03f3c391807cd
Author: jfeher <feh...@gmail.com>
Date: 2016-09-12T09:57:44Z
[FLINK-4613] Extend ALS for implicit case
XtX matrix precomputation is not yet done.
----
> Extend ALS to handle implicit feedback datasets
> -----------------------------------------------
>
> Key: FLINK-4613
> URL: https://issues.apache.org/jira/browse/FLINK-4613
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Gábor Hermann
> Assignee: Gábor Hermann
>
> The Alternating Least Squares implementation should be extended to handle
> _implicit feedback_ datasets. These datasets do not contain explicit ratings
> by users, they are rather built by collecting user behavior (e.g. user
> listened to artist X for Y minutes), and they require a slightly different
> optimization objective. See details by [Hu et
> al|http://dx.doi.org/10.1109/ICDM.2008.22].
> We do not need to modify much in the original ALS algorithm. See [Spark ALS
> implementation|https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala],
> which could be a basis for this extension. Only the updating factor part is
> modified, and most of the changes are in the local parts of the algorithm
> (i.e. UDFs). In fact, the only modification that is not local, is
> precomputing a matrix product Y^T * Y and broadcasting it to all the nodes,
> which we can do with broadcast DataSets.
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