The implicit rankings are the output of Tf-idf. I.e.: Each_ranking= frecuency of an ítem * log(amount of total customers/amount of customers buying the ítem)
El 14 sept. 2016 17:14, "Sean Owen" <[email protected]> escribió: > What are implicit rankings here? > RMSE would not be an appropriate measure for comparing rankings. There are > ranking metrics like mean average precision that would be appropriate > instead. > > On Wed, Sep 14, 2016 at 9:11 PM, Pasquinell Urbani < > [email protected]> wrote: > >> It was a typo mistake, both are rmse. >> >> The frecency distribution of rankings is the following >> >> [image: Imágenes integradas 2] >> >> As you can see, I have heavy tail, but the majority of the observations >> rely near ranking 5. >> >> I'm working with implicit rankings (generated by TF-IDF), can this affect >> the error? (I'm currently using trainImplicit in ALS, spark 1.6.2) >> >> Thank you. >> >> >> >> 2016-09-14 16:49 GMT-03:00 Sean Owen <[email protected]>: >> >>> There is no way to answer this without knowing what your inputs are >>> like. If they're on the scale of thousands, that's small (good). If >>> they're on the scale of 1-5, that's extremely poor. >>> >>> What's RMS vs RMSE? >>> >>> On Wed, Sep 14, 2016 at 8:33 PM, Pasquinell Urbani >>> <[email protected]> wrote: >>> > Hi Community >>> > >>> > I'm performing an ALS for retail product recommendation. Right now I'm >>> > reaching rms_test = 2.3 and rmse_test = 32.5. Is this too much in your >>> > experience? Does the transformation of the ranking values important for >>> > having good errors? >>> > >>> > Thank you all. >>> > >>> > Pasquinell Urbani >>> >> >> >
