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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Popularity of recommender items
Date Sat, 08 Feb 2014 20:13:21 GMT
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 <pat@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.dunning@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 <pat@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
>
>

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