This all describes how the implementation operates, logically. The matrix P is never formed, for sure, certainly not by the caller.
The implementation actually extends to handle negative values in R too but it's all taken care of by the implementation. On Thu, Feb 12, 2015 at 11:29 PM, Crystal Xing <crystalxin...@gmail.com> wrote: > HI Sean, > > I am reading the paper of implicit training. > > Collaborative Filtering for Implicit Feedback Datasets > > It mentioned > > "To this end, let us introduce > a set of binary variables p_ui, which indicates the preference of user u to > item i. The p_ui values are derived by > binarizing the r_ui values: > p_ui = 1 if r_ui > 0 > and > > p_ui=0 if r_ui = 0 > > " > > > If for user_item without interactions, I do not include it in the training > data. All the r_ui will >0 and all the p_ui is always 1? > Or the Mllib's implementation automatically takes care of those no > interaction user_product pairs ? > > > On Thu, Feb 12, 2015 at 3:13 PM, Sean Owen <so...@cloudera.com> wrote: >> >> Where there is no user-item interaction, you provide no interaction, >> not an interaction with strength 0. Otherwise your input is fully >> dense. >> >> On Thu, Feb 12, 2015 at 11:09 PM, Crystal Xing <crystalxin...@gmail.com> >> wrote: >> > Hi, >> > >> > I have some implicit rating data, such as the purchasing data. I read >> > the >> > paper about the implicit training algorithm used in spark and it >> > mentioned >> > the for user-prodct pairs which do not have implicit rating data, such >> > as no >> > purchase, we need to provide the value as 0. >> > >> > This is different from explicit training where when we provide training >> > data, for user-product pair without a rating, we just do not have them >> > in >> > the training data instead of adding a user-product pair with rating 0. >> > >> > Am I understand this correctly? >> > >> > Or for implicit training implementation in spark, the missing data will >> > be >> > automatically filled out as zero and we do not need to add them in the >> > training data set? >> > >> > Thanks, >> > >> > Crystal. > > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
