This is a fine way to go (and I have often (mis)used search engines as recommendation engines).
Another angle is to consider the item level recommendations for a single item to simply be additional attributes. You can also look at user level cooccurrence analysis of attributes (including SVD) as simply a way to smooth out the attributes a bit so that sparsity doesn't take such a big bite out of serendipity. This makes cooccurrence analysis look a whale of a lot like anchor text propagation which speaks to your final point. On Tue, Jan 26, 2010 at 6:15 PM, Jake Mannix <jake.man...@gmail.com> wrote: > On Tue, Jan 26, 2010 at 3:36 PM, Ted Dunning <ted.dunn...@gmail.com> > wrote: > > > I define it a bit differently by redefining recommendations as machine > > learning. > > > > On Tue, Jan 26, 2010 at 1:44 PM, Sean Owen <sro...@gmail.com> wrote: > > > > > I would narrow and specify this, in the context of Mahout, to have a > > > collaborative filtering angle: > > > > Since Ted (Mr. Machine Learning) wants to describe content-based > recommendations > as machine learning, and Sean (Mr. Taste/CF) goes and describes it it terms > of > collaborative filtering, I suppose I'll put on my "search guy" hat, and > describe it the > way I see it: > -- Ted Dunning, CTO DeepDyve
