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https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15528764#comment-15528764
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ASF GitHub Bot commented on FLINK-4613:
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Github user thvasilo commented on the issue:
https://github.com/apache/flink/pull/2542
Hello @gaborhermann thank you for your contribution!
Are the numbers here non-zero entries in a matrix?
If that is the case do you think it would be possible to test this on some
larger scale datasets?
This would bring it closer to actual use cases someone using Flink might
have.
> 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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