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From Noelia Osés Fernández <no...@vicomtech.org>
Subject Re: Which template for predicting ratings?
Date Thu, 23 Nov 2017 14:09:12 GMT
May I please get an answer to this question? I have a project that depends
on the answer to this question.

Using the Recommendation template (https://github.com/apache/
incubator-predictionio-template-recommender) and the ecom recs template (
https://github.com/apache/incubator-predictionio-template-ecom-recommender)
*why
are the predictions outputted by the algorithm outside of the range of the
input data?*

*Are the predictions of this algorithm bounded?* How can I know what the
bounds are?

If not, how can I make the predictions be in the same range as the input
data?

Thank you very much!

On 14 November 2017 at 16:45, Noelia Osés Fernández <noses@vicomtech.org>
wrote:

> Thanks Pat.
>
> I am now using the Recommendation template (http://predictionio.
> incubator.apache.org/templates/recommendation/quickstart/) (
> https://github.com/apache/incubator-predictionio-template-recommender). I
> believe this template uses MLlib ALS.
>
> I am using the movielens ratings data. In the sample that I'm using, the
> minimum rating is 0.5 and the max is 5.
>
> However, the predictions returned by the recommendation engine are above
> 5. For example:
>
> Recommendations for user: 1
>
> {"itemScores":[{"item":"2492","score":8.760136688429496},{"
> item":"103228","score":8.074123814810278},{"item":"2907","score":7.
> 659090305689766},{"item":"6755","score":7.65084600130184}]}
>
> Shouldn't these predictions be in the range from 0.5 to 5 ?
>
>
>
> On 13 November 2017 at 18:53, Pat Ferrel <pat@occamsmachete.com> wrote:
>
>> 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 <pat@occamsmachete.com> 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 <noses@vicomtech.org>
>> 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 <pat@occamsmachete.com> 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-corre
>>> lated-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 <noses@vicomtech.org>
>>> 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
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>>
>> --
>> <http://www.vicomtech.org/>
>>
>> Noelia Osés Fernández, PhD
>> Senior Researcher |
>> Investigadora Senior
>>
>> noses@vicomtech.org
>> +[34] 943 30 92 30
>> Data Intelligence for Energy and
>> Industrial Processes | Inteligencia
>> de Datos para Energía y Procesos
>> Industriales
>>
>> <https://www.linkedin.com/company/vicomtech>
>> <https://www.youtube.com/user/VICOMTech>
>> <https://twitter.com/@Vicomtech_IK4>
>>
>> member of:  <http://www.graphicsmedia.net/>     <http://www.ik4.es/>
>>
>> Legal Notice - Privacy policy
>> <http://www.vicomtech.org/en/proteccion-datos>
>>
>>
>>
>
>
> --
> <http://www.vicomtech.org>
>
> Noelia Osés Fernández, PhD
> Senior Researcher |
> Investigadora Senior
>
> noses@vicomtech.org
> +[34] 943 30 92 30
> Data Intelligence for Energy and
> Industrial Processes | Inteligencia
> de Datos para Energía y Procesos
> Industriales
>
> <https://www.linkedin.com/company/vicomtech>
> <https://www.youtube.com/user/VICOMTech>
> <https://twitter.com/@Vicomtech_IK4>
>
> member of:  <http://www.graphicsmedia.net/>     <http://www.ik4.es>
>
> Legal Notice - Privacy policy
> <http://www.vicomtech.org/en/proteccion-datos>
>



-- 
<http://www.vicomtech.org>

Noelia Osés Fernández, PhD
Senior Researcher |
Investigadora Senior

noses@vicomtech.org
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos para Energía y Procesos
Industriales

<https://www.linkedin.com/company/vicomtech>
<https://www.youtube.com/user/VICOMTech>
<https://twitter.com/@Vicomtech_IK4>

member of:  <http://www.graphicsmedia.net/>     <http://www.ik4.es>

Legal Notice - Privacy policy <http://www.vicomtech.org/en/proteccion-datos>

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