To summarize -- For purposes of the book, I'm going to have to talk about content-based, but can't really say there's support for it since there isn't. That I think is just fine; better to write this up in 2 pages than be silent.
Separately there absolutely no reason to exclude content-based techniques, no. It's just a question of where the pieces are on the to-do list. Some forms of content-based recommendation already work in the framework: 1) if your items are really dominated by one attribute (e.g. recommending songs based on artist) then by thinking of that attribute as the 'item' and applying regular CF, you're doing content-based recommendation 2) if you want to base item-item similarity on attributes and pair that with item-based CF, you're doing content-based recommendation and then there are things that probably should be there but aren't yet: 3) something that extract user->attribute and attribute->item associations based on user->item associations, and does something like CF based on it and then everything else is, to me, a question mark for later. Thanks for the good discussion. It's clarified and enhanced my thinking and will let me write a good couple pages in the current draft. Sean On Thu, Jan 28, 2010 at 2:29 AM, Jake Mannix <jake.man...@gmail.com> wrote: > On Wed, Jan 27, 2010 at 6:18 PM, Ted Dunning <ted.dunn...@gmail.com> wrote: >> >> >> a) whether items that are retrieved/recommended are opaque or have >> attributes (is the process content-based?) >> > > Well this is the part we all are agreeing on - we're all talking about the > "new" > (to our framework) technique of including content attributes. Good here. > > >> b) whether the basis for retrieving/recommending items is an explicit query >> (of whatever form) or is an implicit query formed by the user's historical >> actions (is this search or recommendation?) >> > > I'm *not* suggesting we consider explicitly chose attributes that the user > enters on their own. That is completely a search, not a recommendation, > and isn't what the recommendation part of Mahout is about. I'm talking > about queries generated in some way from the content of the thing which > wants to have recommendations given to it (the webpage which wants ads, > the job posting which wants applicants, the user who has a profile who > wants XYZ, etc...). Definitely implicit, in terms of what users *do*, but > possibly fairly explicit in terms of what the *are*. > > >> c) whether the retrieval/recommendation of items uses the behavior of all >> users to sharpen the results (is this a social algorithm or not?) >> > > Right, this is the part which makes it a CF-based approach, and I would > like us to not get caught up in this being the focal point of a > recommendation > system. > > >> d) what do we call the system (recommendation, collaborative filtering, >> search or whatever) >> > > I think we're all cool with calling it a recommendation if it's 1) not > specifically > user-driven (your point b. above), yet results of some type are delivered. > > -jake >
