Have a look at the PlusAnonymousUserDataModel, which is a bit of a hack but a decent sort of solution for this case. It lets you temporarily add a user to the system and then everything else works as normal, so you can make recommendations to these new / temp users.
There isn't a way to inject anything but rating/pref information directly, no. You can use this info in a Rescorer to influence recommendations; this is not specific to the case of a new user. You can also decide to make "recommendations" by a completely different means for new users -- for example, some canned list of top-10 recs that is appropriate for their city or referring site. That's legitimate too in practice. Yes you can also find most-similar users based on this info. You'd have to write the similarity metric yourself. I assume this is also not the metric you use in your real recommender. So maybe you could use it to find the nearest 1 real user and sub in those recommendations? or a neighborhood. You would have to rewrite a bit of what a recommender does to go this way but it's not so hard. No there is no content-based similarity metric; this is so domain specific. On Wed, Jul 4, 2012 at 4:54 PM, Matt Mitchell <goodie...@gmail.com> wrote: > Hi, > > Slowly prototyping a recommendation here. The system does not have > user accounts. Since the users on the system don't have accounts, I'm > struggling a bit with completely new users, and what to recommend > them. I do have information about the user, like what referring site > they came from (1 of n partner sites), the city they want to shop in, > and rating value of the product. I wonder if I could use this > information, to find the most similar existing user. Then use that > most similar user to generate recommendations? Anyone have tips for > dealing this this? > > I'm not sure if Mahout supports finding most "similar user" based on > user attributes, if not, this should be a simple sql/where-like select > from the database. > > - Matt
