occamsmachete.com>
Cc: user@predictionio.apache.org <user@predictionio.apache.org>
<user@predictionio.apache.org>
Subject: Re: UR evaluation
Very nice article. And it gets much clearer the importance of treating the
recommendation like a ranking task.
Thanks
Il gio 10 mag 2018, 19:1
apache.org>
> Date: May 10, 2018 at 9:54:23 AM
> To: Pat Ferrel <p...@occamsmachete.com> <p...@occamsmachete.com>
> Cc: user@predictionio.apache.org <user@predictionio.apache.org>
> <user@predictionio.apache.org>
> Subject: Re: UR evaluation
>
> thank you very much, i
3 AM
To: Pat Ferrel <p...@occamsmachete.com>
Cc: user@predictionio.apache.org <user@predictionio.apache.org>
Subject: Re: UR evaluation
thank you very much, i didn't see this tool, i'll definitely try it. Clearly
better to have such a specific instrument.
2018-05-10 18:36
ctionio.apache.org <user@predictionio.apache.org>
<user@predictionio.apache.org>
Subject: UR evaluation
hi all, i successfully trained a universal recommender but i don't know how
to evaluate the model.
Is there a recommended way to do that?
I saw that *predictionio-template-recommender* ac
hi all, i successfully trained a universal recommender but i don't know how
to evaluate the model.
Is there a recommended way to do that?
I saw that *predictionio-template-recommender* actually has
the Evaluation.scala file which uses the class *PrecisionAtK *for the
metrics.
Should i use this
