Thanks Ted, that's the kind of information I was looking for. I was aware of R, but it never occurred to me to look into using it for this purpose. Thanks for your suggestion and the time taken to reply to me, very helpful.
Best of luck with the project! Kind regards, Graham >From: Ted Dunning <[email protected]> > To: [email protected], [email protected] > >I don't think that the belief network stuff is going to give you much lift. >It also sounds like you don't have many other users looking at the data at >the same time so you probably don't have much crowd-source data to work >with. > >My feeling is that you should use a standard machine learning technique such >as SVM or logistic regression to build your model based on whatever features >you have available. There are a number of ways you could implement this. > >One us to simply run R as a server and access it from java. That would give >you the quickest and simplest way to implement the machine learning part of >what you are doing with the least hassle in your swing app. Rserve let's >you do this with a minimum of fuss. I would preprocess the data for your >10,000 instances off-line and store them in the R environment. Then, >everytime the user does something interesting, you can pass their entire >history to the R server, rerun learning from scratch and then evaluate all >instances again, passing the top few back to your swing program. > >Mahout also has a few learning algorithms in preliminary form, but I really >think that you would do better with your small dataset to simply use >standard algorithms in R.
