>No, you're welcome to make comparisons in these tables. It's valid. Okay I think I'm back at square one. So we have an AAD using an Euclidean similarity measure of 1.2 This is calculated for ratings in the range of 1 through to 10. For the same data we also have a Tanimoto AAD of 1.3
Now imagine the ratings are now in the range of 1 through to 20 but all the users rate in exactly the same way (rating value)*2 We would now have for the Euclidean driven recommender an AAD of 2.4 but the tanimoto would still be 1.3 How can we use AAD to compare the two recommenders ? A bit of background just to explain why I'm labouring this point (and I'm well aware that I'm labouring it) By being able to describe AAD as "the amount a prediction would differ from the actual rating. (Lower the better)" to a business stake holder makes the evaluation of the recommender vivid and concrete. The confidence this creates is not to be under-estimated. However how do I describe to a business stake holder the meaning of a tanimoto produced AAD? I can't at the moment :-) cheers Lee C
