Thompson sampling doesn't require time other than a sense of what do we now know. It really is just a correct form for dithering that uses our current knowledge.
For a worked out version of Thompson sampling with ranking, see this blog: http://tdunning.blogspot.com/2013/04/learning-to-rank-in-very-bayesian-way.html The reason that we aren't adding this like cross-rec and other things is that "we" have full-time jobs, mostly. Suneel is full-time on Mahout, but the rest are not. You seem more active than most. On Sat, Feb 8, 2014 at 8:50 AM, Pat Ferrel <p...@occamsmachete.com> wrote: > 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 <ted.dunn...@gmail.com> 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 <p...@occamsmachete.com> 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 > >
