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[24.18.213.211]) by smtp.gmail.com with ESMTPSA id w20sm35050826pfi.89.2017.11.13.09.53.13 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Mon, 13 Nov 2017 09:53:14 -0800 (PST) From: Pat Ferrel Message-Id: <37C43283-3B9C-4EDC-9E26-09ED84B1A91F@occamsmachete.com> Content-Type: multipart/alternative; boundary="Apple-Mail=_140BFA8E-5D85-4932-AB86-B9196B32F5DF" Mime-Version: 1.0 (Mac OS X Mail 10.3 \(3273\)) Subject: Re: Which template for predicting ratings? Date: Mon, 13 Nov 2017 09:53:13 -0800 In-Reply-To: <1AEA4152-EA26-4A3F-BEFE-4A8046A35212@occamsmachete.com> Cc: user@predictionio.incubator.apache.org To: user@predictionio.apache.org References: <1AEA4152-EA26-4A3F-BEFE-4A8046A35212@occamsmachete.com> X-Mailer: Apple Mail (2.3273) archived-at: Mon, 13 Nov 2017 17:53:28 -0000 --Apple-Mail=_140BFA8E-5D85-4932-AB86-B9196B32F5DF Content-Transfer-Encoding: quoted-printable Content-Type: text/plain; charset=utf-8 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 Os=C3=A9s Fern=C3=A1ndez = > 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.=20 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=E2=80=99t = 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.=20 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-occure= nces/ = This uses data from a public web site to show significant lift = by using =E2=80=9Clike=E2=80=9D and =E2=80=9Cdislike=E2=80=9D in = recommendations. This used the Universal Recommender. On Nov 10, 2017, at 5:02 AM, Noelia Os=C3=A9s Fern=C3=A1ndez = > 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 --=20 Noelia Os=C3=A9s Fern=C3=A1ndez, PhD Senior Researcher | Investigadora Senior noses@vicomtech.org +[34] 943 30 92 30 Data Intelligence for Energy and Industrial Processes | Inteligencia de Datos para Energ=C3=ADa y Procesos Industriales = = member of: Legal Notice - Privacy policy = --Apple-Mail=_140BFA8E-5D85-4932-AB86-B9196B32F5DF Content-Transfer-Encoding: quoted-printable Content-Type: text/html; charset=utf-8 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 Os=C3=A9s Fern=C3=A1ndez <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=E2=80=99t 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 = =E2=80=9Clike=E2=80=9D and =E2=80=9Cdislike=E2=80=9D in recommendations. = This used the Universal Recommender.


On Nov 10, 2017, at 5:02 AM, = Noelia Os=C3=A9s Fern=C3=A1ndez <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









--

Noelia = Os=C3=A9s Fern=C3=A1ndez, PhD
Senior Researcher |
Investigadora = Senior

noses@vicomtech.org
+[34] 943 30 92 30
Data Intelligence for Energy and
Industrial Processes | Inteligencia
de Datos = para Energ=C3=ADa y Procesos
Industriales

  

member of:     

Legal Notice - Privacy = policy


= --Apple-Mail=_140BFA8E-5D85-4932-AB86-B9196B32F5DF--