No.
It is uniformly better to have (item+action, user). In fact, I would
prefer to have it the other way around when describing it to match the
matrix row x column convention.
(user, item+action) where action is binary leads to A = [A_1 | A_2] = user
by 2xitem. The alternative of (user+action, item) leads to
[ A_1 ]
A = [ ] = 2xuser by item
[ A_2 ]
This last form doesn't have a uniform set of users to connect the items
together. When you compute the cooccurrence matrix you get A_1' A_1 + A_2'
A_2 which gives you recommendations from 1=>1 and from 2=>2, but no
recommendations 1=>2 or 2=>1. Thus, no cross recommendations.
On Sat, Feb 23, 2013 at 10:39 AM, Pat Ferrel <[email protected]> wrote:
> But the discussion below lead me to realize that cf/taste is doing
> something in addition to [B'B] h_p, which returns user history based recs.
> I'm getting better results currently from item similarity based recs, which
> I blend with user-history based recs. To get item similarity based recs
> cf/taste is using a similarity metric and I'd guess that it uses the input
> matrix to get these results (something like the dot product for cosine).
> For item similarity should I create a training set of (item, user+action)?
