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
