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From Philippe Adjiman <adji...@gmail.com>
Subject Re: Implicit feedback with varying significance
Date Thu, 17 Mar 2011 11:08:11 GMT
Hi,

You might find that high quality paper interesting:
http://research.yahoo.com/pub/2433 for the part of your problem about
associating varying confidence levels to different implicit feedback
signals.

As far as I know, no algorithm in mahout is ready to use for the model
exposed in that paper, yet.

I plan to re-read that paper soon as i might need to solve similar problems
in the near future, would be happy to discuss my experience with it when
i'll get there.


Best,
-Philippe.

-- 
Philippe Adjiman | twitter: padjiman | linkedin:
il.linkedin.com/in/philippeadjiman | blog: http://philippeadjiman.com/blog



On Thu, Mar 17, 2011 at 12:28 PM, Sebastian Schelter <ssc@apache.org> wrote:

> Hi,
>
> I have some questions about how to handle implicit feedback with varying
> significance in an e-commerce environment.
>
> Say I have an onlineshop and I track views and purchases of products.
>
> Tracked views are like a two-edged sword then, on the one hand they are
> very useful because you get a lot of them quickly and can use them to tackle
> the cold-start problem (you should already have enough data to find similar
> items from view data the day after the product was put online). On the other
> hand the most co-viewed stuff will be from the same category of things and
> will narrow the similar items to that category. This might become worse over
> time as recommenders tend to amplify themselves.
>
> After some time we should have purchase data for that new items, which is
> expected to have a higher significance because a higher engagement of the
> users is involved. I'd like to give these signals a much greater weight to
> broaden up the recommendations, especially to find interesting
> cross-category similarities. My worry is that these are currently
> "overruled" by the sheer amount of view data points and I found out that
> simple procedures like applying business specific rules to filter the
> similar items to only include cross-category pairs doesn't really help with
> that problem.
>
> Does somebody have an idea (or better something learned from experience)
> how to proceed to solve that problem?
>
> A simple approach I have in mind would be to separately handle similar
> items based on views and similar items based on purchases.
>
> --sebastian
>



-- 
Philippe Adjiman | twitter: padjiman | linkedin:
il.linkedin.com/in/philippeadjiman | blog: http://philippeadjiman.com/blog

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