Not now, but see https://issues.apache.org/jira/browse/SPARK-3066
As an aside, it's quite expensive to make recommendations for all
users. IMHO this is not something to do, if you can avoid it
architecturally. For example, consider precomputing recommendations
only for users whose probability of
Thanks, Sean! Glad to know it will be in the future release.
On Thu, Feb 12, 2015 at 2:45 PM, Sean Owen so...@cloudera.com wrote:
Not now, but see https://issues.apache.org/jira/browse/SPARK-3066
As an aside, it's quite expensive to make recommendations for all
users. IMHO this is not
Hi all - I've spent a while playing with this. Two significant sources of speed
up that I've achieved are
1) Manually multiplying the feature vectors and caching either the user or
product vector
2) By doing so, if one of the RDDs is a global it becomes possible to
parallelize this step by
