t user and product.
> > If we don't have any data trained on a user, there is no way to predict
> how he would like a product.
> > That filtering takes a lot of work though. I can share some code on that
> too if you like.
> >
> > Best,
> > Burak
> >
&
ny data trained on a user, there is no way to predict how
> he would like a product.
> That filtering takes a lot of work though. I can share some code on that too
> if you like.
>
> Best,
> Burak
>
> - Original Message -
> From: "Jay Hutfles"
> To: us
that too if
you like.
Best,
Burak
- Original Message -
From: "Jay Hutfles"
To: user@spark.apache.org
Sent: Thursday, August 7, 2014 1:06:33 PM
Subject: questions about MLLib recommendation models
I have a few questions regarding a collaborative filtering model, and was
hoping for some rec
On Thu, Aug 7, 2014 at 9:06 PM, Jay Hutfles wrote:
> 0,0,5
> 0,1,5
> 0,2,0
> 0,3,0
> 1,0,5
> 1,3,0
> 2,1,4
> 2,2,0
> 3,0,0
> 3,1,0
> 3,2,5
> 3,3,4
> 4,0,0
> 4,1,0
> 4,2,5
> val rank = 10
This is likely the problem? your rank is actually larger than the
number of users or items. The error could p
I have a few questions regarding a collaborative filtering model, and was
hoping for some recommendations (no pun intended...)
*Setup*
I have a csv file with user/movie/ratings named unimaginatively
'movies.csv'. Here are the contents:
0,0,5
0,1,5
0,2,0
0,3,0
1,0,5
1,3,0
2,1,4
2,2,0
3,0,0
3,1,0
