Hi Deb, thanks for sharing your result. Please find my comments inline in
blue.
Best regards,
Wei
From: Debasish Das
To: Wei Tan/Watson/IBM@IBMUS,
Cc: Xiangrui Meng , "user@spark.apache.org"
Date: 08/17/2014 08:15 PM
Subject: Re: MLLib: implementing ALS with d
rson/us-wtan*
> <http://researcher.ibm.com/person/us-wtan>
>
>
>
> From:Xiangrui Meng
> To: Wei Tan/Watson/IBM@IBMUS,
> Cc: "user@spark.apache.org"
> Date:08/04/2014 12:51 AM
> Subject:Re: MLLib: implementing ALS with d
uot;
Date: 08/04/2014 12:51 AM
Subject: Re: MLLib: implementing ALS with distributed matrix
To be precise, the optimization is not `get all products that are
related to this user` but `get all products that are related to users
inside this block`. So a product factor won't be s
To be precise, the optimization is not `get all products that are
related to this user` but `get all products that are related to users
inside this block`. So a product factor won't be sent to the same
block more than once. We considered using GraphX to implement ALS,
which is much easier to unders
Hi,
I wrote my centralized ALS implementation, and read the distributed
implementation in MLlib. It uses InLink and OutLink to implement functions
like "get all products which are related to this user", and ultimately
achieves model distribution.
If we have a distributed matrix lib, the c
