Re: Best way to do a recommendation engine based on CLR (Click Through Rate)

[Ted Dunning <te...@gmail.com>]

Do your users click more than once on an item?

If you do intend to use total clicks, at least normalize them by number of
appearances.

Better still, if you have some indicator that the click wasn't adventitious,
use that.  Once upon a time, for instance, we tried clicks on a video.  It
became clear later that successfully watching 30 seconds of video was a much
better indicator since it avoided problems with misleading titles and
possibly even broken videos.  Using a javascript timer to emit a beacon hit
after sitting on a page for a bit is one way to get this kind of info on a
normal web-site.

I generally would be against counting clicks but would rather just collect a
1 if clicks(user, item) > 0


On Tue, Jul 27, 2010 at 1:55 PM, Simon Reavely <si...@gmail.com>wrote:

> Hi,
>
> I am wondering what the best way is to implement recommendations based on
> click through rates. What I have:
> - user id
> - resource (i.e. item)
> - click through count for user on that resource
>
> I'm reading Mahout in Action MEAP right now (very good so far). Mahout
> seems
> to be very preference based (votings/ratings) but I know (reading Mahout in
> Action) that it also supports preference-less recommendations. However,
> since I have a click through count preference-less recommendation seems to
> be throwing away this click through data.
>
> I wondered if I can somehow convert click through count to a preference or
> if I should take another approach.
>
> Some ideas I had:
> - Just use the click through count as the preference (knowing that
> different
> users will have widely different counts).
> - Normalize the click count across users to say a 0-100 scale
> - ok, that's it...only two ideas so far!
>
> Any suggestions/patterns?
> Any warnings/anti-patterns?
>
> It seems like this should be a really common use-case for recommendations.
>
> Cheers,
> Simon
>
> --
> Simon Reavely
> simon.reavely@gmail.com
>