I can only quantify this very, very roughly. In my experience it takes 5-10 people on a single item to make recommendations work with the LLR metrics. From this and the long-tail characteristics, I think you can take it back to a real sparsity number, but I think that this is probably easy enough to work with.
Sean can probably give you a more broad perspective since he has worked with a variety of different systems and different scales. 2010/7/29 Matthias Böhmer <[email protected]> > > A quick test would be to consider two clicks to be required as a measure > of > > interest. My guess is that your data will suddenly become too sparse to > > use. > > Can you quantify this? Lets say a recommender system has n users and m > items. Is it possible con conclude, how much data (ratings) you need > to run recommendations? Are there any rough estimations for judging > the sparsity of the data? > >
