A-ha. I should elaborate then. The essence of the item-based algorithm is estimating prefs as weighted averages of other prefs. The weights are similarities. This depends on having prefs to average in the first place in the data model. But it doesn't depend on whether the similarity value uses ratings or not. Those weights are just weights, wherever they come from.
The recommenders that operate without ratings don't actually compute a weighted average anymore -- it doesn't make sense. They compute something else to rank on, but it's no longer an estimate pref actually. That's why AAD doesn't have real meaning there. But in either case you're welcome to use log-likelihood similarity for example which does not depend on pref values at all. It's just supplying a value which is used as a weight in the first instance, and something else in the second instance. On Wed, Oct 26, 2011 at 12:35 PM, lee carroll <[email protected]> wrote: >> AAD is not valid for comparison when you're not using >>ratings in your *recommender*. It's nothing to do with your similarity >>metric. > > The penny drops.
