How do 'stacked' recommenders (like the Netflix winners) work? On Wed, Jul 20, 2011 at 9:22 PM, Jamey Wood <[email protected]> wrote: > Great. Thanks, Ted! > > --Jamey > > On Wed, Jul 20, 2011 at 9:57 PM, Ted Dunning <[email protected]> wrote: > >> Oh... you do have to be careful with this a bit because some of these side >> factors can have disastrously non-sparse characteristics. For instance, a >> large fraction of the people in the world are in each age range. Likewise, >> there are entirely too many romance novels in the world. These issues of >> prevalence can seriously impact your algorithm run-time (adversely). You >> can compensate for this by sampling or just recognizing that such pervasive >> features inherently cannot be very useful since too many things would be >> recommended. >> >> On Wed, Jul 20, 2011 at 8:51 PM, Ted Dunning <[email protected]> >> wrote: >> >> > Yes. This can work. And it can go both ways since you might do >> something >> > like combine recommendations for a specific book with more general >> > recommendations for a specific author or genre. You can also have >> > recommendations for, say, an author or genre based on demographic >> quantities >> > such as geo-location or age range. >> > >> > It can be a bit tricky to combine all of these features. One principled >> > way would be to extend the log-linear latent factor approach to include >> > these multiple cross terms. A less principled, but pretty effective >> method >> > is to score all kinds of recommendations independently and then >> recalibrate >> > based on percentiles (if you can make sense of that, often not possible) >> or >> > by some declining function of rank. >> > >> > >> > On Wed, Jul 20, 2011 at 7:18 PM, Jamey Wood <[email protected]> >> wrote: >> > >> >> Is there any precedent for treating users' demographic characteristics >> as >> >> items (particularly for item-based recommendation)? For example, if one >> >> were performing item-based recommendation within a bookselling site, >> it'd >> >> be >> >> natural to include the user:item purchases as boolean preferences. But >> >> could it also make sense to include certain user:demographic pairs as >> >> boolean preferences (e.g. user123:age40-to-50)? Of course, these items >> >> would need to be filtered (by a Rescorer) in the recommendation outputs, >> >> but >> >> I'm curious whether including them as inputs is potentially helpful. >> >> >> >> Thanks, >> >> Jamey >> >> >> > >> > >> >
-- Lance Norskog [email protected]
