Well, you could broadly call all machine learning "analysis and optimization" of a sort! What do you mean, specifically? If you mean you expect this to compute online in real-time rather than off-line, in batch, as the output of some standalone tool -- it is online. You're suppose to query these in realtime. The offline parts of this project are the Hadoop-based ones.
There is also no such thing as an "anonymous user" in collaborative filtering per se. It makes no difference whether a user has signed up for an account or not. As long as you can assign some ID to the user, you can inject it into this framework and use any algorithm you want. So in that sense, the whole thing supports anonymous users, and I don't understand the comment. PAUDM is a practical 'hack' to speed up inclusion of such a user. It's useful if not strictly required. It is not as if without that, only users with an account can be used. I'm sorry you're struggling, but it would be more useful to be specific. I've used this particular hack in 2 systems, and helped create 4 others without any particular use of 'anonymous users' and they all most certainly had a point! On Sun, Sep 19, 2010 at 12:21 AM, Lance Norskog <[email protected]> wrote: > I don't see the point of a recommender system without the anonymous user > feature. Otherwise it's just a data analysis/optimization tool and there are > plenty of simpler ways to do that. > > Lance
