ality?
Do you have other smart tips to handle our memory problem?
Best regards, Niklas
2017-01-15 22:30 GMT+01:00 Pat Ferrel <p...@occamsmachete.com>:
>
> > On Jan 14, 2017, at 2:41 AM, Niklas Ekvall <niklas.ekv...@gmail.com>
> wrote:
> >
> > Thanks again Pat!
uality based on only one event far in the
> past. CCO using all the clickstream (or important parts of it) can do quite
> well.
>
> This may seem an edge case but only in degree, every ecom app has data
> they are throwing away and CCO addresses this.
>
> On Dec 13, 2016, at
ind spark-itemsimilarity to serve
> recommendations. Read about the UR here: http://actionml.com/docs/ur <
> http://actionml.com/docs/ur>
>
> On Nov 30, 2016, at 6:58 AM, Niklas Ekvall <niklas.ekv...@gmail.com>
> wrote:
>
> I found that you can, so ignore my questio
I found that you can, so ignore my question!
Best reagrds, Niklas
2016-11-30 15:42 GMT+01:00 Niklas Ekvall <niklas.ekv...@gmail.com>:
> Hello!
>
> I'm using *spark-itemsimilarity *to produce related recommendations and
> the input data has the form *userID, itemID. *Could I
Hello!
I'm using *spark-itemsimilarity *to produce related recommendations and the
input data has the form *userID, itemID. *Could I also use the from *userID,
itemID, value* (value > 0)? Or does *spark-itemsimilarity* only handles
binary values?
Best regards, Niklas
;
> > On Nov 24, 2015, at 12:21 PM, Niklas Ekvall <niklas.ekv...@gmail.com>
> wrote:
> >
> > Okay!
> >
> > No pre-filter and the user/item ids should start from 0 and go as many
> user
> > and items there are. So, all the data we have should go into
te.com');>> wrote:
> Do your ids start with 0 and cover all numbers between 0 and the number of
> items -1 (same for user ids)?
> The old hadoop-mahout code required ordinal ids starting at 0
>
>
> On Nov 24, 2015, at 8:19 AM, Niklas Ekvall <niklas.ekv...@gmail.com>
> wr
, November 24, 2015, Pat Ferrel <p...@occamsmachete.com> wrote:
> I wouldn’t pre-filter but in any case the ids input to hadoop-mahout need
> to follow those rules.
>
> The new recommender I mentioned has no such requirements, it uses string
> IDs.
>
> On Nov 24, 2015,
thub.com/PredictionIO/template-scala-parallel-universal-recommendation
> a single machine install script is here: https://docs.prediction.io/start/
>
> On Nov 24, 2015, at 2:16 AM, Niklas Ekvall <niklas.ekv...@gmail.com>
> wrote:
>
> Hello Mahout Users!
>
> I use today Mah
recommendations in
this list the best one or is there some randomness in this list?
Best regards,
Niklas Ekvall
on the subject.
And, to answer your question, cooccurrence recommendation works great with
diverse sources of behavior.
On Sun, Apr 6, 2014 at 8:40 PM, Niklas Ekvall niklas.ekv...@gmail.com
wrote:
Thanks Pat!
I did find a book by Ted Dunning and Ellen Friedman (Practical Machine
Hi Pat and Ted!
Yes I agree with about the rank and MAP. But in this case, that is a good
initial guess on the parameters *number of features* and *lambda*?
Where can I find the best article about cooccurrence recommender? And can
one use this approach for different types of data, e.g., ratings,
:
On Apr 6, 2014, at 2:48 AM, Niklas Ekvall niklas.ekv...@gmail.com
wrote:
Hi Pat and Ted!
Yes I agree with about the rank and MAP. But in this case, that is a good
initial guess on the parameters *number of features* and *lambda*?
20 or 30 features depending on the variance in your
Hi,
My name is Niklas Ekvall and I have a implementation of the recommender
algorithm Large-scale Parallel Collaborative Filtering for the Netflix
Prize and now I'm wondering how to choose the number of features and
lambda. Could any of guys help me to explain a stepwise strategy to choose
.
--sebastian
On 03/30/2014 11:53 AM, Niklas Ekvall wrote:
Hi,
My name is Niklas Ekvall and I have a implementation of the recommender
algorithm Large-scale Parallel Collaborative Filtering for the Netflix
Prize and now I'm wondering how to choose the number of features and
lambda. Could
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