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List:       kde-active
Subject:    new Contourd();
From:       Karlheinz.Hohm () basyskom ! de (Karlheinz Hohm)
Date:       2011-09-06 16:31:53
Message-ID: 1334071172.37142.1315326713858.JavaMail.open-xchange () ext01 ! basyskom ! de
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?

On September 6, 2011 at 12:03 PM Ivan Cukic <ivan.cukic at kde.org> wrote:

>
> I've pushed the new contour daemon which unlike it was advertised doesn't
> break everything. The dataengine is patched to work with the new daemon
> and currently it shows some recommendations (at least on the desktop -
> will test on exo when the image is available).?
very nice!
we have created a new MeeGo image which you can use for testing, you
can?download the latest MeeGo Plasma Active image from:

http://share.basyskom.com/contour/Deployment/latest-meego-plasma-contour-in-progress.html??
Cheers,
Karlheinz.
?
?

> > There are currently two recommendation engines:
> - the links (scripted) shows a few links (meant for testing purposes only)
> - the mediapause - currently works with bangarang - while the player is
> running, it shows the option to pause it. Completely useless, but since
> there is no light sensor on the desktop, I wasn't able to do it like we
> talked about. Will do it once this gets on exo.
>
> The scored documents engine will come soon :) after that, we are on the
> same level as the old contour daemon (ok, not the same, a few ft above)
>
> ** Scoring the recommendation engines
>
> I'm currently working on making the recommendation engines scored when the
> user chooses an action.
>
> The method is as follows:
>
> 1 - the chosen engine's score is increased by a constant called $learning$
> 2 - the list of recommendations is iterated - for each do:
>? ? - 2.1 - decrease the score of the engine by $learning * exp(-x)$
>? ? ? ? ? ? ? where x is the order of the recommendation in question,
>? ? ? ? ? ? ? starting with 1
>
> How does this work?
>
> The first thing is that what we are trying to keep the sum of scores after
> the change the same as before. It doesn't really work, but it is close
> enough since integral of exp(-x) from 0 to Inf is 1 - we don't have 1, but
> something lower since in our case, we have a discrete sum of a finite
> number of elements - exp(-1) + exp(-2) + ... + exp(-number_of_recomms).
>
> Effects
>
> - If an engine is too confident and gives a high score to its result, but
> that result was not chosen, it will be punished more than other results
> due to the exp(-x) part.
> - the more recommendations an engine gives, the higher punishment it will
> receive in the case none of its recomms are selected.
> - if the engine behaves like in the previous example, but one of its
> recomms is selected, it will have an increased score, but not as increased
> as the engine that made only one recomm. This is due to the fact that
> although it will get a reward for the good suggestion, it will get
> punished for the bad ones as well.
>
> I've been testing this on the two engines we have, and it is quite fun :)
>
>
> Cheerio,
> Ivan
>
> p.s. I friggin' love the exp function :)
>
>
> --
> So remember when you're feeling very small and insecure
> How amazingly unlikely is your birth
> And pray that there's intelligent life somewhere up in space
> Because there's bugger all down here on earth.
>? ? ?-- Monty Python
>
> _______________________________________________
> Active mailing list
> Active at kde.org
> https://mail.kde.org/mailman/listinfo/active
>?
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