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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Malicious users on recommender system
Date Wed, 29 Aug 2012 06:16:30 GMT
First off, it looks like Amazon is not filtering for engagement here.

Second, you have to have Amazon's prominence before attacks by large groups
of people are worth it.

Third, to quote Amazon "these happen once in a blue moon".  That means you
can correct for them manually.

So pragmatically speaking, this isn't a big deal if you do the basics right.

On Tue, Aug 28, 2012 at 11:23 PM, Zia mel <ziad.kamel25@gmail.com> wrote:

> Thanks Ted. If you can please elaborate on this , Let's say for
> example I am recommending online books and 1000 users joined and added
> most of the popular books to their list and rate them high to be
> similar to other users , then they start adding books they want to
> advertise , how can I detect this attitude ? and how can I know if
> these are malicious users or true users that just have common
> interests ? Is there a way that I can solve this case that happened to
> Amazon
>   http://news.cnet.com/2100-1023-976435.html
>
> Thanks
>
>
>
>
> On Tue, Aug 28, 2012 at 8:23 PM, Ted Dunning <ted.dunning@gmail.com>
> wrote:
> > The single most effective thing you can do with malicious users like this
> > is to let them think that they have won.  In the ideal case, you can
> detect
> > simple click frauds and maintain a per user play adjustment so that they
> > see the fraudulent stats and everybody else sees the corrected stats.  If
> > you can, this should even extend to your leader board pages.  Once you
> have
> > this, the fraudsters will generally not increase the sophistication of
> > their attacks and you have a fairly simple situation.
> >
> > You also will have a bit of an advantage if you pick a metric that
> > indicates fairly serious engagement.  With videos, for instance, I have
> > used plays > 30 seconds as the metric and this was handled by a beacon on
> > the page while the 30 second delay measurement was on the server side.
> >  This requires a browser to be live and in focus for 30 seconds in order
> to
> > get a play event which substantially increases the cost of committing the
> > click fraud on the fraudsters side.
> >
> > With the recommendation analysis itself, the key is to flatten all
> > frequency metrics per user.  With unsophisticated click fraud, the abuse
> > will center on creating high play frequencies for a few users which will
> > then be counted as a very small input signal since so few users are doing
> > it and their high play rates won't matter.  Also, the major effect if any
> > will be to simply give the fraudsters recommendations for their own items
> > which will make them happy and won't matter to anyone else.
> >
> > On Tue, Aug 28, 2012 at 6:29 PM, Zia mel <ziad.kamel25@gmail.com> wrote:
> >
> >> Hi ,
> >>
> >> Is there any way to check for malicious users in mahout so I can
> >> remove them from the recommendations or reduce their effect ?
> >> Malicious users are the ones that want to play with the ratings and
> >> increase or downgrade it.
> >>
> >> Thanks,
> >>
>

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