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From Noelia Osés Fernández <no...@vicomtech.org>
Subject Re: Which template for predicting ratings?
Date Mon, 13 Nov 2017 10:42:17 GMT
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-
> 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 <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

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