Michael, very good point. thank you
David, I am simply asking from a general perspective, in a recommendations 
based system, how does escape from the feedback loop that is generated from 
a successful recommendations engine suggesting clothes, then those clothes 
get purchased more, then the recommendatiosn engine continue to suggest 
those exact clothes.



On Friday, January 3, 2014 8:39:40 AM UTC-6, Michael Hunger wrote:
>
> Also the data the recommendation is based on is not static and not only 
> relying on the recommendations.
>
> So there are people buying, liking etc. new products come in some are 
> promoted, there are hypes etc.
>
> Lots of variables that affect the data that is fed into the 
> recommendation, and then people don't necessarily choose what they are 
> recommended.
>
> So it is more like an emergent behavior of a more or less chaotic system.
>
> It might end up us all watching Game of Thrones but then that's what you 
> should do :)
>
> But as it filters out things you've already did the next recommendation 
> for you will be something new.
>
> HTH
>
> Michael
>
> Am 03.01.2014 um 15:12 schrieb M. David Allen 
> <allen....@gmail.com<javascript:>
> >:
>
> Javad,
>
> The graph database would probably just store the list of things you could 
> choose amongst for your recommendations; a graph database doesn't do 
> recommendations, and hence whether or not those recommendations would 
> reenforce one another would depend on the kind of algorithm you're using 
> for doing the recommendation.  If it was done incorrectly, yes the problem 
> you're asking about could occur, but my guess is that it wouldn't have 
> anything to do with neo4j.
>
> Can you describe more what you're trying to do?
>
>
>
> On Thursday, January 2, 2014 2:50:47 PM UTC-5, Javad Karabi wrote:
>>
>> When using a graph database for a recommendations engine, would not the 
>> suggested recommendations continue to reenforce themselves, thus kind of 
>> creating a feedback loop?
>> How does one prevent this from happening?
>>
>
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