Many thanks! On Thu, Feb 12, 2015 at 3:31 PM, Sean Owen <so...@cloudera.com> wrote:
> 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. > > > > >
