It's fairly meaningless, as there are no prefs in this case, so no such thing as estimated prefs to compare against real ones. The recommender does rank on a metric, but it's not estimate pref in this case. I imagine it will spit out a number but it's not going to be of much use.
All you can really do here is use precision/recall tests. On Tue, Oct 25, 2011 at 6:50 PM, lee carroll <[email protected]> wrote: > What does the metric returned by > AverageAbsoluteDifferenceRecommenderEvaluator mean for non rating > based recommenders. > > The Mahout in action book describes the metric as being the amount a > prediction would differ from the actual rating. (Lower the better) > But what does that mean in terms of a recommender which uses a > similarity measure which does not use rating data, such as jaccard > or for that matter measures which use rank. > > Example: > Say we get a 1.2 AAD for a recommender using Euclidean distance. > Ratings range from 1 to 10 so i'm thinking this is pretty good, we are > out by a little over 1. We will make the mistake of > thinking around 6 or 8 when its the actual preference is a seven. > > But > > What does a 1.3 AAD for a Tanimoto using recommender mean? and can I > compare it with other recommender AAD's? (I'm sure you can, as the > excellent mahout book does :-) > > What am I missing? do I have a to simplistic view of the metric of AAD? > > Thanks in advance Lee C >
