I can say I think I get the gist of what these approaches are and why they work but would need time to study to really understand!
I am particularly intrigued at the moment by this last question, of how to pick a sample of very different items. Is the idea here that you look at items as vectors of preferences, and try to find the most-orthogonal subset of them? Gram-Schmidt would be changing the vectors rather than selecting them, so I am curious how these two things connect. It is a really good problem I think. On Jun 19, 2009 1:00 PM, "Ted Dunning" <[email protected]> wrote: With good item-item recommendations, there are good heuristic approaches to this. They reduce to a binary and approximate version of Gram-Schmidt orthogonalization.
