Can you tell us which type of algorithm you are using? Depending on what you are using that will affect the answer.
On Wed, Aug 29, 2012 at 7:16 AM, Ted Dunning <[email protected]> wrote: > 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 <[email protected]> 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 <[email protected]> > > 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 <[email protected]> > 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, > > >> > > >
