OK, got your points, thanks Ferrel and Peng. On Sun, Sep 21, 2014 at 11:39 PM, Pat Ferrel <[email protected]> wrote:
> @li > > Mahout down-samples the input data based on how “important” the > cooccurrence of interactions seems to be. I’d use SIMILARITY_LOGLIKELIHOOD > for the best measure of this. You will still get no recs for some users if > they don’t have enough interactions or they are not “important” > interactions. > > On Sep 15, 2014, at 7:38 PM, Peng Zhang <[email protected]> wrote: > > As far as I know, mahout would not do any post processing to remove > records from recs. I assume you refer to recs as recommendation candidates. > If you find some user is not in recs (the output), please make sure he/she > has sufficient interactions with the items. > > On Sep 16, 2014, at 10:17 AM, Wei Li <[email protected]> wrote: > > > Thanks Peng. > > > > Yes, I agree your points, there may not be enough interactions between > > users and items to do the recommendations, but do our Mahout code does > some > > extra filtering to remove the recommendation candidates? > > > > On Mon, Sep 15, 2014 at 3:35 PM, Peng Zhang <[email protected]> > wrote: > > > >> Mahout does not guarantee specified recs for each user. There are many > >> reasons, e.g, there might not be enough similar users or items for a > user. > >> > >> Peng Zhang > >> > >> -- > >> Sent from my iPhone > >> > >>> On Sep 15, 2014, at 3:15 PM, Wei Li <[email protected]> wrote: > >>> > >>> Hi Mahout Users: > >>> > >>> We are using the RecommderJob to perform the item-based > >>> recommendations, the following settings are used: > >>> > >>> similairtyClassname=SIMILARITY_COOCCURRENCE > >>> numRecommendations=20 > >>> other parameters are set to default values > >>> > >>> while we see that the size of the recommendation results for some users > >> is > >>> less than 20, only 1 or 2. Since we have no time to dive into the > source > >>> code now, we do know if we see the right parameters. Does any one can > >> help > >>> us on this issue? many thanks :) > >>> > >>> Best > >>> Wei > >> > > >
