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From Sean Owen <>
Subject Re: Best way to do a recommendation engine based on CLR (Click Through Rate)
Date Wed, 28 Jul 2010 07:31:11 GMT
Yes, preferences are merely an indicator of the strength of an
association. They aren't necessarily from explicit ratings; you could
base this figure on click through counts.

You do not need to scale the values; the particular scale does not
matter to any algorithm.

However one important lesson is that mapping associations to numbers
in the 'wrong' way can significantly harm the result. For example, 1
click is much much more significant than 0. 2 clicks is more
significant than 1. But are 10 clicks 5 times stronger than 2?
probably not. Maybe it's a favorite, but after "several" clicks the
additional clicks mean little more.

So for instance I might begin your experiments by using the log of the
click count as the pref value.

And then there's other issues like normalizing, throwing out spurious
clicks and spam, etc.


On Tue, Jul 27, 2010 at 11:55 PM, Simon Reavely <> 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

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