Sean, Responses inline.
Yep, I know what you mean. I didn't really talk about this (yet) in > the book and likely should spend a page on it. > That would be great. I mean, the two discovery problems are 'user' discovery and 'item' discovery. Using content-based approaches, I can do something about when to recommend a new item... but the user approach example would be great. Of course, I wouldn't want to talk you out of documenting the best-practice for Mahout based item discovery in the final version of the book. > One solution is to punt on the problem, as Ted says: until you get > enough data or the user has a real presence, don't recommend. > In my case, this isn't practical. For now, (and in my home grown, deprecated recommender) I have a "backfill list" (built with system-wide stats) that I use to make a recommendation when I can't come up with anything from my existing recommender... but the main point of my email was that I may want to get rid of this and do as much as possible inside of Mahout. > ... > > I can offer you a mildly hacky but fairly useful middle-ground: > PlusAnonymousUserDataModel. It decorates/wraps your DataModel and lets > you temporarily set preferences for one anonymous new user. (You need > to think of thread-safety potentially -- you can only do this for one > user at a time.) > > This would be ideal. I will be using a default userid of 0 for anonymous users, and as far as the application is concerned, I can treat all anonymous users as the same anonymous user for now. ... > Appreciated.
