In point 1, I don't think I'd say it that way. It's not true that test/training is divided by user, because every user would either be 100% in the training or 100% in the test data. Instead you hold out part of the data for each user, or at least, for some subset of users. Then you can see whether recs for those users match the held out data.
Yes then you see how the held-out set matches the predictions by computing ratios that give you precision/recall. The key question is really how you choose the test data. It's implicit data; one is as good as the next. In the framework I think it just randomly picks a subset of the data. You could also split by time; that's a defensible way to do it. Training data up to time t and test data after time t. On Fri, Jun 7, 2013 at 7:51 PM, Michael Sokolov <msoko...@safaribooksonline.com> wrote: > I'm trying to evaluate a few different recommenders based on boolean > preferences. The in action book suggests using an precision/recall metric, > but I'm not sure I understand what that does, and in particular how it is > dividing my data into test/train sets. > > What I think I'd like to do is: > > 1. Divide the test data by user: identify a set of training data with data > from 80% of the users, and test using the remaining 20% (say). > > 2. Build a similarity model from the training data > > 3. For the test users, divide their data in half; a "training" set and an > evaluation set. Then for each test user, use their training data as input > to the recommender, and see if it recommends the data in the evaluation set > or not. > > Is this what the precision/recall test is actually doing? > > -- > Michael Sokolov > Senior Architect > Safari Books Online >
