Google's wide and deep recommender model sounds really cool, but I'm struggling to believe I'm grasping the wide section right so wanted to check my understanding.
Their paper says the following:
The wide component consists of the cross-product transformation of user installed apps and impression apps
Each example corresponds to one impression
Let's say we have 5 apps, A through E. My understanding is that the cross-product transformation would represent that as 20 columns, representing each possible combination of
impressed app (making 25, but then presumably the 5 "matching" cross-products like
and(installed=App_A, impressed=App_A) would be removed because presumably Google is smart enough not to impress Apps the user already has). Let's also say we have 3 Users, called X - Z. X has installed apps A and C, and is shown app B and D. Y has installed App B and is shown A and E. Z has installed apps A, C and D and is shown apps B and E. With that dataset, the cross-product transformation should look (I think) like this:
My question is; is my understanding of the transformation there correct? If so that's going to be one gigantic matrix in fairly short order, particularly given they have over a billion users and a million different apps.