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
