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From "Joseph K. Bradley (JIRA)" <>
Subject [jira] [Commented] (SPARK-3066) Support recommendAll in matrix factorization model
Date Mon, 09 Mar 2015 15:33:38 GMT


Joseph K. Bradley commented on SPARK-3066:

Oops, true, not an actual metric.  LSH sounds reasonable.  Do you know of use cases or how
well it's been found to work for recommendation problems?

> Support recommendAll in matrix factorization model
> --------------------------------------------------
>                 Key: SPARK-3066
>                 URL:
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Xiangrui Meng
>            Assignee: Debasish Das
> ALS returns a matrix factorization model, which we can use to predict ratings for individual
queries as well as small batches. In practice, users may want to compute top-k recommendations
offline for all users. It is very expensive but a common problem. We can do some optimization
> 1) collect one side (either user or product) and broadcast it as a matrix
> 2) use level-3 BLAS to compute inner products
> 3) use Utils.takeOrdered to find top-k

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