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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_r87353332 --- Diff: flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/recommendation/ALS.scala --- @@ -156,6 +171,26 @@ class ALS extends Predictor[ALS] { this } + /** Sets the input observations to be implicit, thus using the iALS algorithm for learning. --- End diff -- Good point. Changed the wording. > 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)