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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-4613) Extend ALS to handle implicit feedback datasets
Date Tue, 29 Nov 2016 15:17:58 GMT

    [ https://issues.apache.org/jira/browse/FLINK-4613?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15705574#comment-15705574

ASF GitHub Bot commented on FLINK-4613:

Github user mbalassi commented on the issue:

    Yes @gaborhermann , I finally got 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. 

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