Re: Which template for predicting ratings?

[Pat Ferrel]

What I was saying is the UR can use ratings, but not predict them. Use MLlib 
ALS recommenders if you want to predict them for all items.


On Nov 13, 2017, at 9:32 AM, Pat Ferrel  wrote:

What we did in the article I attached is assume 1-2 is dislike, and 4-5 is like.

These are treated as indicators and will produce a score from the recommender 
but these do not relate to 1-5 scores.

If you need to predict what the user would score an item MLlib ALS templates 
will do it.



On Nov 13, 2017, at 2:42 AM, Noelia Osés Fernández > wrote:

Hi Pat,

I truly appreciate your advice.

However, what to do with a client that is adamant that they want to display the 
predicted ratings in the form of 1 to 5-stars? That's my case right now. 

I will pose a more concrete question. Is there any template for which the 
scores predicted by the algorithm are in the same range as the ratings in the 
training set?

Thank you very much for your help!
Noelia

On 10 November 2017 at 17:57, Pat Ferrel > wrote:
Any of the Spark MLlib ALS recommenders in the PIO template gallery support 
ratings.

However I must warn that ratings are not very good for recommendations and none 
of the big players use ratings anymore, Netflix doesn’t even display them. The 
reason is that your 2 may be my 3 or 4 and that people rate different 
categories differently. For instance Netflix found Comedies were rated lower 
than Independent films. There have been many solutions proposed and tried but 
none have proven very helpful.

There is another more fundamental problem, why would you want to recommend the 
highest rated item? What do you buy on Amazon or watch on Netflix? Are they 
only your highest rated items. Research has shown that they are not. There was 
a whole misguided movement around ratings that affected academic papers and 
cross-validation metrics that has fairly well been discredited. It all came 
from the Netflix prize that used both. Netflix has since led the way in 
dropping ratings as they saw the things I have mentioned.

What do you do? Categorical indicators work best (like, dislike)or implicit 
indicators (buy) that are unambiguous. If a person buys something, they like 
it, if the rate it 3 do they like it? I buy many 3 rated items on Amazon if I 
need them. 

My advice is drop ratings and use thumbs up or down. These are unambiguous and 
the thumbs down can be used in some cases to predict thumbs up: 
https://developer.ibm.com/dwblog/2017/mahout-spark-correlated-cross-occurences/ 

 This uses data from a public web site to show significant lift by using “like” 
and “dislike” in recommendations. This used the Universal Recommender.


On Nov 10, 2017, at 5:02 AM, Noelia Osés Fernández > wrote:


Hi all,

I'm new to PredictionIO so I apologise if this question is silly.

I have an application in which users are rating different items in a scale of 1 
to 5 stars. I want to recommend items to a new user and give her the predicted 
rating in number of stars. Which template should I use to do this? Note that I 
need the predicted rating to be in the same range of 1 to 5 stars.

Is it possible to do this with the ecommerce recommendation engine?

Thank you very much for your help!
Noelia









-- 
 

Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior

no...@vicomtech.org 
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos para Energía y Procesos
Industriales

   
  

member of:   

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Re: Does PIO support [ --master yarn --deploy-mode cluster ]?

[Pat Ferrel]

yarn-cluster mode is supported but extra config needs to be set so the driver 
can be run on a remote machine.

I have seen instructions for this on the PIO mailing list.



On Nov 12, 2017, at 7:30 PM, wei li  wrote:

Hi Pat
Thanks a lot for your advice.

We are using [yarn-client] mode now, UR trains well and we can monitor the 
output log at pio application console.

I tried to find a way to use [yarn-cluster] mode, to submit a train job and 
shutdown the pio application (in docker) immediately.
(monitor the job process at hadoop culster website instead of pio application 
console).
But then I met errors like this: file path [file://xxx.jar] can not be found.

Maybe,  [yarn-cluster] mode is not supported now. I will keep looking for the 
explanation.


在 2017年11月11日星期六 UTC+8上午12:41:33，pat写道：
Yes PIO support Yarn but you may have more luck getting an explanation on the 
PredictionIO mailing list.
Subscribe here: http://predictionio.incubator.apache.org/support/ 


On Nov 9, 2017, at 11:33 PM, wei li  wrote:

Hi, all

Any one have any idea about this?

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Re: "pio app delete" breaks my PIO installation

[Noelia Osés Fernández]

I forgot to mention that *pio status* reports my system is all ready to go
:(

but it isn't true. I can't import the data.

On 13 November 2017 at 16:47, Noelia Osés Fernández 
wrote:

> Hi,
>
> It has happened several times already that after I execute:
>
> *pio app delete appname*
>
> my PIO installation breaks. Does anybody else have this problem?
>
> Particularly, this time I get the following error during data import:
>
> Traceback (most recent call last):
>   File "data/import_eventserver.py", line 63, in 
> import_events(client, args.file)
>   File "data/import_eventserver.py", line 33, in import_events
> properties= { "rating" : float(data[2]) }
>   File "/usr/local/lib/python2.7/dist-packages/predictionio/__init__.py",
> line 247, in create_event
> event_time).get_response()
>   File "/usr/local/lib/python2.7/dist-packages/predictionio/connection.py",
> line 111, in get_response
> self._response = self.rfunc(tmp_response)
>   File "/usr/local/lib/python2.7/dist-packages/predictionio/__init__.py",
> line 118, in _acreate_resp
> (response.error, response.request))
> predictionio.NotCreatedError: Exception happened: timed out for request
> POST /events.json?accessKey=XQewLhG4RfqP1zAMH9y3E5c4wd0_
> vFYRYgQIMX3gxluzNlTI6N_M16z_CjjV9zAY {'event': 'rate', 'eventTime':
> '2017-11-13T15:32:24.506+', 'entityType': 'user', 'targetEntityId':
> '31', 'properties': {'rating': 2.5}, 'entityId': '1', 'targetEntityType':
> 'item'} /events.json?accessKey=XQewLhG4RfqP1zAMH9y3E5c4wd0_
> vFYRYgQIMX3gxluzNlTI6N_M16z_CjjV9zAY?event=rate=
> 2017-11-13T15%3A32%3A24.506%2B=user&
> targetEntityId=31=%7B%27rating%27%3A+2.5%7D&
> entityId=1=item
>
>
> This app was working this morning. I deleted it, then created it again and
> now I have this error and can't make it work.
>
> Furthermore, when I execute
>
>
> *curl -i -X GET http://localhost:7070 *
>
> the terminal just hangs there, it doesn't print any output nor error
> messages.
>
> Any help is much appreciated,
> Noelia
>
>
>
>
>
>
>
>


-- 


Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior

no...@vicomtech.org
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos para Energía y Procesos
Industriales





member of:   

Legal Notice - Privacy policy 

Re: Which template for predicting ratings?

[Noelia Osés Fernández]

Hi Pat,

I truly appreciate your advice.

However, what to do with a client that is adamant that they want to display
the predicted ratings in the form of 1 to 5-stars? That's my case right
now.

I will pose a more concrete question. *Is there any template for which the
scores predicted by the algorithm are in the same range as the ratings in
the training set?*

Thank you very much for your help!
Noelia

On 10 November 2017 at 17:57, Pat Ferrel  wrote:

> Any of the Spark MLlib ALS recommenders in the PIO template gallery
> support ratings.
>
> However I must warn that ratings are not very good for recommendations and
> none of the big players use ratings anymore, Netflix doesn’t even display
> them. The reason is that your 2 may be my 3 or 4 and that people rate
> different categories differently. For instance Netflix found Comedies were
> rated lower than Independent films. There have been many solutions proposed
> and tried but none have proven very helpful.
>
> There is another more fundamental problem, why would you want to recommend
> the highest rated item? What do you buy on Amazon or watch on Netflix? Are
> they only your highest rated items. Research has shown that they are not.
> There was a whole misguided movement around ratings that affected academic
> papers and cross-validation metrics that has fairly well been discredited.
> It all came from the Netflix prize that used both. Netflix has since led
> the way in dropping ratings as they saw the things I have mentioned.
>
> What do you do? Categorical indicators work best (like, dislike)or
> implicit indicators (buy) that are unambiguous. If a person buys something,
> they like it, if the rate it 3 do they like it? I buy many 3 rated items on
> Amazon if I need them.
>
> My advice is drop ratings and use thumbs up or down. These are unambiguous
> and the thumbs down can be used in some cases to predict thumbs up:
> https://developer.ibm.com/dwblog/2017/mahout-spark-
> correlated-cross-occurences/ This uses data from a public web site to
> show significant lift by using “like” and “dislike” in recommendations.
> This used the Universal Recommender.
>
>
> On Nov 10, 2017, at 5:02 AM, Noelia Osés Fernández 
> wrote:
>
>
> Hi all,
>
> I'm new to PredictionIO so I apologise if this question is silly.
>
> I have an application in which users are rating different items in a scale
> of 1 to 5 stars. I want to recommend items to a new user and give her the
> predicted rating in number of stars. Which template should I use to do
> this? Note that I need the predicted rating to be in the same range of 1 to
> 5 stars.
>
> Is it possible to do this with the ecommerce recommendation engine?
>
> Thank you very much for your help!
> Noelia
>
>
>
>
>
>
>


-- 


Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior

no...@vicomtech.org
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos para Energía y Procesos
Industriales





member of:   

Legal Notice - Privacy policy