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 <[email protected]> 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 <[email protected]> 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 <[email protected]> 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 <[email protected]> >> 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 <[email protected]> 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 <[email protected]> >>> 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 >> >> [email protected] >> +[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 > > [email protected] > +[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 [email protected] +[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>
