They are all active in a day. I am talking about 8.3 million active users a day. A significant fraction of them will be new users ( say about 2-3 million of them ). Further the churn on items is likely to make historical recommendations obsolete. Thus if I have recommendations that were good of user A yesterday, they are likely to be far less a weight as of today.
On Tue, Oct 25, 2011 at 11:32 AM, Sean Owen <[email protected]> wrote: > On Tue, Oct 25, 2011 at 4:08 PM, Vishal Santoshi > <[email protected]> wrote: > > In our case the preferences is a user clicking on an article ( which > > doubles as an item ). > > And these articles are introduced at a frequent rate. Thus the number of > new > > items that > > occur in the dataset has a very frequent churn and thus not necessarily > > having any history. > > Of course we need to recommend the latest item. > > OK, but I'm still not seeing why all users need an update every time. > Surely most of the 8.3M users aren't even active in a given day. >
