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https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15651161#comment-15651161
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ASF GitHub Bot commented on FLINK-4613:
---------------------------------------
Github user thvasilo commented on a diff in the pull request:
https://github.com/apache/flink/pull/2542#discussion_r87199446
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/recommendation/ALS.scala
---
@@ -273,6 +308,14 @@ object ALS {
val defaultValue: Option[Int] = Some(10)
}
+ case object ImplicitPrefs extends Parameter[Boolean] {
--- End diff --
Can't find a way to comment on line 264/299 but we should take the
opportunity to set the default number of factors to a more reasonable 50, and
add to the docstring and documentation the recommendation:
> we recommend working with the highest number of factors feasible within
computational limitations.
Which comes straight from the iALS paper.
> 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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