You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "Daniel Li (JIRA)" <ji...@apache.org> on 2017/03/05 10:58:38 UTC
[jira] [Comment Edited] (SPARK-6407) Streaming ALS for
Collaborative Filtering
[ https://issues.apache.org/jira/browse/SPARK-6407?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15896177#comment-15896177 ]
Daniel Li edited comment on SPARK-6407 at 3/5/17 10:57 AM:
-----------------------------------------------------------
{quote}
In practice fold-in works fine. Folding in a day or so of updates has been OK.
The question isn't RMSE but how it affects actual rankings of items in recommendations, and it takes a while before the effect of the approximation actually changes a rank.
{quote}
Hmm, I see. This would be something I'd be interested in implementing for Spark if there's need. Are there implementations (or papers) of this you know of that I could look at?
was (Author: danielyli):
bq. In practice fold-in works fine. Folding in a day or so of updates has been OK.
The question isn't RMSE but how it affects actual rankings of items in recommendations, and it takes a while before the effect of the approximation actually changes a rank.
Hmm, I see. This would be something I'd be interested in implementing for Spark if there's need. Are there implementations (or papers) of this you know of that I could look at?
> Streaming ALS for Collaborative Filtering
> -----------------------------------------
>
> Key: SPARK-6407
> URL: https://issues.apache.org/jira/browse/SPARK-6407
> Project: Spark
> Issue Type: New Feature
> Components: DStreams
> Reporter: Felix Cheung
> Priority: Minor
>
> Like MLLib's ALS implementation for recommendation, and applying to streaming.
> Similar to streaming linear regression, logistic regression, could we apply gradient updates to batches of data and reuse existing MLLib implementation?
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org