On Thu, 19 Apr 2001, Sam Joseph wrote:
> Chris Anderson Wrote
>
> >
> > How are you incorporating this feedback into the rank? Suppose I had
> > another key:
> >
> > utdqti3tro7qiet6atsql2iugakfyg2 Quincy (12), Adams (10), Bush (8)
> >
> > If my query contains the term Adams, it seems the relative weights of
> the
> > term associations are the same, but the absolute strength of the
> Quincy
> > hit is stronger.
>
> Right, actually NeuroGrid uses a more complex ranking system where every
> rank is stored as a vector, so for example we might have:
>
> utdqti3tro7qiet6atsql2iugakfyg2 Quincy (12, 23, 45), Adams (10, 12,
> 25), Bush (8, 100, 1300)
>
> Where the ranks indicate the number of times a user has bookmarked
> something after searching for it with that keyword, the number of times
> it was clicked through after it was searched for using that keyword and
> the number of times it was returned as a search result for that keyword.
> You can then use some prob maths to compare the ranks. See:
>
> http://www.neurogrid.net/WhitePaper0_3.html
>
> For more details of the maths that can be used for this.
>
Gotcha. Any thoughts on profiling users bookmarks to estimate keyword
rankings of new data?
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