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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Which template for predicting ratings?
Date Mon, 13 Nov 2017 17:53:13 GMT
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 <mailto: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 <mailto: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/
<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 <mailto: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









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