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 <p...@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 <no...@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 no...@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>
