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 >> > >
