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https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15653691#comment-15653691
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ASF GitHub Bot commented on FLINK-4613:
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Github user tillrohrmann commented on the issue:
https://github.com/apache/flink/pull/2542
@thvasilo you're right in the future we should make sure that we properly
document how we came up with the testing data.
What I've done to come up with the testing data for the `ALSITSuite` is to
factorize the given recommendation matrix with Matlab with the given parameters
(factors, iterations, etc). I've implemented ALS in Matlab for that. Ideally,
we use a library for that so that others can reproduce the test results. I can
share the ALS implementation if you like.
Ideally we do something similar for iALS.
> 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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