Didn’t mean to imply I had historical view data—yet. The Thompson sampling ‘trick’ looks useful for auto converging to the best of A/B versions and a replacement for dithering. Below you are proposing another case to replace dithering—this time on a list of popular items? Dithering works on anything you can rank but Thompson Sampling usually implies a time dimension. The initial guess, first Thompson sample, could be thought of as a form of dithering I suppose? Haven’t looked at the math but it wouldn’t surprise me to find they are very similar things.
While we are talking about it, why aren’t we adding things like cross-reccomendations, dithering, popularity, and other generally useful techniques into the Mahout recommenders? All the data is there to do these things, and they could be packaged in the same Mahout Jobs. They seem to be languishing a bit while technology and the art of recommendations moves on. If we add temporal data to preference data a bunch of new features come to mind, like hot lists or asymmetric train/query preference history. On Feb 6, 2014, at 9:43 PM, Ted Dunning <[email protected]> wrote: One way to deal with that is to build a model that predicts the ultimate number of views/plays/purchases for the item based on history so far. If this model can be made Bayesian enough to sample from the posterior distribution of total popularity, then you can use the Thomson sampling trick and sort by sampled total views rather than estimated total views. That will give uncertain items (typically new ones) a chance to be shown in the ratings without flooding the list with newcomers. Sent from my iPhone > On Feb 7, 2014, at 3:38, Pat Ferrel <[email protected]> wrote: > > The particular thing I’m looking at now is how to rank a list of items by > some measure of popularity when you don’t have a velocity. There is an > introduction date though so another way to look at popularity might be to > decay it with something like e^-t where t is it’s age. You can see the decay > in the views histogram
