mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Pat Ferrel <>
Subject Re: Popularity of recommender items
Date Sat, 08 Feb 2014 16:50:10 GMT
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 <> 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 <> 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

View raw message