I could see it working, but mainly for queries that have one clearly-best result, or where the 'snippet' itself answers the query. So perhaps for 'answers' search more than general web search.
The implicit user feedback created would be great... but uses might be frustrated by not being able to scan many results quickly. (I always set Google to return 100 results...)
At the start, it would suck, but if the search engine reached 98% accuracy rate, you don't need more results. The engine could even learn when results are in order - for example, asking "where is mongolia" will give you a single answer, but "tv review sites" will give you a list of search results.
So bootstrapping is a challenge. Will users wait out the 'training' period?
(Or similarly, if a forward-thinking group is willing to help train, are their choices representative enough of what the impatient masses eventually want?)
I think it's a promising area for experimentation.
I am going to model this mathematically soon, but with a very small sample size, you can quickly reach a pretty accurate result. And in general, most results are pretty clear - number of states in the u.s will always be the same, no matter how forward thinking you are.
Such an engine has to start small and be trained for a few months, otherwise users will think it sucks. As it grows, it adapts.
I bet one could actually do this very easily using yahoo BOSS. Want to take a stab at it? If anyone wanted to work on this, I'd help, so long it was written in python.
I could see it working, but mainly for queries that have one clearly-best result, or where the 'snippet' itself answers the query. So perhaps for 'answers' search more than general web search.
The implicit user feedback created would be great... but uses might be frustrated by not being able to scan many results quickly. (I always set Google to return 100 results...)