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From Gokhan Capan <gkhn...@gmail.com>
Subject Re: Decaying score for old preferences when using the .refresh()
Date Fri, 08 Nov 2013 07:36:52 GMT
On Fri, Nov 8, 2013 at 6:24 AM, Ted Dunning <ted.dunning@gmail.com> wrote:

> On Thu, Nov 7, 2013 at 12:50 AM, Gokhan Capan <gkhncpn@gmail.com> wrote:
>
> > This particular approach is discussed, and proven to increase the
> accuracy
> > in "Collaborative filtering with Temporal Dynamics" by Yehuda Koren. The
> > decay function is parameterized per user, keeping track of how consistent
> > the user behavior is.
> >
>
> Note that user-level temporal dynamics does not actually improve the
> accuracy of ranking. It improves the accuracy of ratings.


Yes, the accuracy of rating prediction.

 Since
> recommendation quality is primarily a precision@20 sort of activity,
> improving ratings does no good at all.


> Item-level temporal dynamics is a different beast.
>

I think the intuition here is, when making an item neighborhood base
recommendation, to penalize the contribution of the items that the user has
rated a long time ago. I didn't test this in a production recommender
system, but I believe this might result in recommendation lists with better
conversion rates in certain use cases.

Best

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