For the past few weeks, I've been having trouble generating consistent constrained queries in our CBR rig, ``W''. For half the datasets we'd generate recommendations that wouldn't map back to the test set, preventing further analysis. The following char should shed some light on the problem:
For the longest time the concern was with the size of our datasets, given that we weren't returning historical results. Upon closer inspection, the datasets giving us trouble contain a very large number of attribute values, and currently ``W'' only learns single-value constraints. This explains why when we constrain our query from learned data we return no results.
Solution: discretize the data, or build a better matching system.