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https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15653555#comment-15653555
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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_r87354805
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/recommendation/ALS.scala
---
@@ -535,8 +581,17 @@ object ALS {
itemOut: DataSet[(Int, OutBlockInformation)],
userIn: DataSet[(Int, InBlockInformation)],
factors: Int,
- lambda: Double, blockIDPartitioner: FlinkPartitioner[Int]):
+ lambda: Double, blockIDPartitioner: FlinkPartitioner[Int],
+ implicitPrefs: Boolean,
+ alpha: Double):
DataSet[(Int, Array[Array[Double]])] = {
+ // retrieve broadcast XtX matrix in implicit case
+ val XtXtoBroadcast = if (implicitPrefs) {
--- End diff --
I tried to fit the notation of the explicit ALS code, as it uses `userXtX`
notation in the `updateFactors` function. I think it might be confusing to use
two different notations is the code, even if the paper uses another notation.
> 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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