[
https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15705605#comment-15705605
]
ASF GitHub Bot commented on FLINK-4613:
---------------------------------------
Github user mbalassi commented on a diff in the pull request:
https://github.com/apache/flink/pull/2542#discussion_r90032424
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/recommendation/ALS.scala
---
@@ -581,6 +637,16 @@ object ALS {
val userXy = new ArrayBuffer[Array[Double]]()
val numRatings = new ArrayBuffer[Int]()
+ var precomputedXtX: Array[Double] = null
+
+ override def open(config: Configuration): Unit = {
+ // retrieve broadcasted precomputed XtX if using implicit
feedback
+ if (implicitPrefs) {
+ precomputedXtX =
getRuntimeContext.getBroadcastVariable[Array[Double]]("XtX")
+ .iterator().next()
+ }
+ }
+
override def coGroup(left: lang.Iterable[(Int, Int,
Array[Array[Double]])],
--- End diff --
I agree with @gaborhermann here.
> 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)
