Sean, re my point earlier do you know a more efficient way to compute top k for each user, other than to broadcast the item factors?
(I guess one can use the new asymmetric lsh paper perhaps to assist) — Sent from Mailbox On Thu, Oct 30, 2014 at 11:24 PM, Sean Owen <so...@cloudera.com> wrote: > MAP is effectively an average over all k from 1 to min(# > recommendations, # items rated) Getting first recommendations right is > more important than the last. > On Thu, Oct 30, 2014 at 10:21 PM, Debasish Das <debasish.da...@gmail.com> > wrote: >> Does it make sense to have a user specific K or K is considered same over >> all users ? >> >> Intuitively the users who watches more movies should get a higher K than the >> others... >>
