You will have to get the two user factor vectors from the ALS model and
compute the cosine similarity between them. You can do this using Breeze
vectors:
import breeze.linalg._
val user1 = new DenseVector[Double](userFactors.lookup("user1").head)
val user2 = new DenseVector[Double](userFactors.lookup("user2").head)
val sim = user1.t * user2 / (norm(user1)* norm(user2))
There is no built-in way currently to compute user or item similarities,
though there is a PR working on it:
https://github.com/apache/spark/pull/3536
On Sun, Apr 19, 2015 at 7:29 PM, Christian S. Perone <
christian.per...@gmail.com> wrote:
> The easiest way to do that is to use a similarity metric between the
> different user factors.
>
> On Sat, Apr 18, 2015 at 7:49 AM, riginos <samarasrigi...@gmail.com> wrote:
>
>> Is there any way that i can see the similarity table of 2 users in that
>> algorithm? by that i mean the similarity between 2 users
>>
>>
>>
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