[
https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15653547#comment-15653547
]
ASF GitHub Bot commented on FLINK-4613:
---------------------------------------
Github user gaborhermann commented on a diff in the pull request:
https://github.com/apache/flink/pull/2542#discussion_r87353990
--- 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 --
I also changed this, but note that this is for the explicit ALS algorithm
too. Do you think it's okay to give this recommendation for explicit case too?
Really high number of factors (without regularization) might lead to
overfitting.
> 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.
--
This message was sent by Atlassian JIRA
(v6.3.4#6332)
