Thanks for the quick answer. I've already followed this tutorial but it doesn't use GraphX at all. My goal would be to work directly on the graph, and not extracting edges and vertices from the graph as standard RDDs and then work on that with the standard MLlib's ALS, which has no interest. That's why I tried with the other implementation, but it's not optimized at all.
I might have gone in the wrong direction with the ALS, but I'd like to see what's possible to do with MLlib on GraphX. Any idea ? 2015-06-18 11:19 GMT+02:00 Akhil Das <[email protected]>: > This might give you a good start > http://ampcamp.berkeley.edu/big-data-mini-course/movie-recommendation-with-mllib.html > its a bit old though. > > Thanks > Best Regards > > On Thu, Jun 18, 2015 at 2:33 PM, texol <[email protected]> wrote: > >> Hi, >> >> I'm new to GraphX and I'd like to use Machine Learning algorithms on top >> of >> it. I wanted to write a simple program implementing MLlib's ALS on a >> bipartite graph (a simple movie recommendation), but didn't succeed. I >> found >> an implementation on Spark 1.1.x >> ( >> https://github.com/ankurdave/spark/blob/GraphXALS/graphx/src/main/scala/org/apache/spark/graphx/lib/ALS.scala >> ) >> of ALS on GraphX, but it is painfully slow compared to the standard >> implementation, and uses the deprecated (in the current version) >> PregelVertex class. >> Do we expect a new implementation ? Is there a smarter solution to do so ? >> >> Thanks, >> Regards, >> Timothée Rebours. >> >> >> >> >> -- >> View this message in context: >> http://apache-spark-user-list.1001560.n3.nabble.com/Machine-Learning-on-GraphX-tp23388.html >> Sent from the Apache Spark User List mailing list archive at Nabble.com. >> >> --------------------------------------------------------------------- >> To unsubscribe, e-mail: [email protected] >> For additional commands, e-mail: [email protected] >> >> > -- Timothée Rebours 13, rue Georges Bizet 78380 BOUGIVAL
