each.
I only use small-sized data set so far, like about 5 users and 5000
products with only about 10 ratings.
Thanks.
On Sat, Mar 19, 2016 at 7:58 PM, Hiroyuki Yamada <mogwa...@gmail.com> wrote:
> Hi,
>
> I'm testing Collaborative Filtering with Milib.
&
Hi,
I'm testing Collaborative Filtering with Milib.
Making a model by ALS.trainImplicit (or train) seems scalable as far as I
have tested,
but I'm wondering how I can get all the recommendation results efficiently.
The predictAll method can get all the results,
but it needs the whole
Hi,
I am trying to work with spark-submit with cluster deploy mode in single
node,
but I keep getting ClassNotFoundException as shown below.
(in this case, snakeyaml.jar is not found from the spark cluster)
===
16/03/12 14:19:12 INFO Remoting: Starting remoting
16/03/12 14:19:12 INFO Remoting:
>
> On Thu, 25 Feb 2016 at 13:26 Sabarish Sasidharan <sabarish@gmail.com>
> wrote:
>
>> I believe the ALS algo expects the ratings to be aggregated (A). I don't
>> see why you have to use decimals for rating.
>>
>> Regards
>> Sab
>>
>> On
Hello.
I just started working on CF in MLlib.
I am using trainImplicit because I only have implicit ratings like page
views.
I am wondering which is a more appropriate form of ratings.
Let's assume that view count is regarded as a rating and
user 1 sees page 1 3 times and sees page 2 twice and
> best result.
>
> I think that generally sparser input needs higher alpha, and maybe
> someone tells me that really alpha should be a function of the
> sparsity, but I've never seen that done.
>
>
>
> On Thu, Feb 25, 2016 at 6:33 AM, Hiroyuki Yamada <mogwa...@gmail.com
Hi, I've been doing some POC for CF in MLlib.
In my environment, ratings are all implicit so that I try to use it with
trainImplicit method (in python).
The trainImplicit method takes alpha as one of the arguments to specify a
confidence for the ratings as described in <
