Hi Moella,
I have wriiten a Dcokerfile for pio0.11.0+postgres, without hdfs, hbase, ES.
https://github.com/BrianOn99/docker-predictionio
For simplicity I pulled JDBC 42.0.0 because the was the default one
specified in the config (that is not good practice). To use
postgresql-42.1.4.jar you need
ly work in this order if you want to take advantage of a
> temporary Spark. PIO is installed on the PS/ES machine and the “driver”
> machine in exactly the same way connecting to the same stores.
>
> Hmm, I should write a How to for this...
>
>
>
> On Sep 20, 2017, at 3:23 AM, Br
Hi,
I would like to be able to train and run model on different machines.
The reason is, on my dataset, training takes around 16GB of memory and
deploying only needs 8GB. In order to save money, it would be better
if only a 8GB memory machine is used in production, and only start a
16GB one perha
recommendations (curated recommendations). We convinced them
> to try without the rules and got a huge improvement. You may not see the
> same thing but testing will tell.
>
>
> On Sep 8, 2017, at 2:20 AM, Brian Chiu wrote:
>
> Hi all.
>
> In a recommender, it is quite co
Hi all.
In a recommender, it is quite common to give a higher priority to
newer items. For example, when User A has created a tweet yesterday
and another tweet today, it is better to recommend the newer one to
the other users (assume the 2 tweets has similar property except
creation date)
Specif
se both likes and follows to recommend either items or users.
> It’s also likely that you can use other data you have. This may be what you
> mean by complex but then you don’t have to use the feature...
>
>
> On Sep 5, 2017, at 2:10 AM, Brian Chiu wrote:
>
> Hi everyone.
>
&g
Hi everyone.
I am trying to use PredictionIO to build a recommender for
social-media-like platform, but as I am new to recommender I would
like to get some suggestion from the community here.
The case is something like Twitter:
- A user can create an item
- A user can like an item
- A user can fo
