Interrogative Search: beyond "did you mean"

To some extent, this happens already, and the user is growing accustomed to it. Google's "did you mean" interrogative has yielded some strange results at times, but it is certainly widely appreciated.

According to researcher Amanda Spink, "stimulating users to talk with someone or thing (agent) about their information problem helps generate terms and look at the results for additional terms". Long Tails and Short Queries

Interrogative interfaces might prove helpful for disambiguation and also for query tuning. Search logs are full of examples of common queries which could be improved by tuning (phrases which should be placed in quotes, phrases which would benefit by a proximity operator, etc). An interrogative system could make suggestions which are likely to be of immediate benefit, and would have the side benefit of teaching the user query construction tips which might otherwise remain buried in help files.

Combining an interrogative query tuning mechanism with live search might also produce some useful results. Live search display allows the user to "try before they buy", and while interacting with the engine a user could examine the potential of query refinements without necessarily losing their progress.

An extreme example of an interrogative interface is the 20 questions game at http://www.20q.net/ which is described as follows: "users log onto the website and play against an artificial intelligence (A.I.) foe. Players think of an animal, vegetable, mineral, or other object and 20Q guesses what the player is thinking in twenty questions or less. And, the more people play, the more the game 'learns.' 20Q.net ... played its 44,000,000th game in September 2006". But is this sufficiently searchlike to be of interest?

What if the same principles were applied to information retrieval? We wouldn't want to answer 20 questions for each query, of course, but if we could get great gain from three or four questions then we would probably learn to love it. And if a system showed continuous improvement, it could be trained by the early adopters and that expertise could be leveraged by novice users in later versions. For instance, a disambiguation system would develop a large library of popular disambiguations to present.

In web search we have seen a series of eras in which one search engine has dominated, and then declined: a Webcrawler era, an AltaVista era, a Yahoo era and a Google era. In each case, one engine has emerged as clearly superior and then gradually lost some (or most) of its luster due to lack of scale, webmaster spamming, or some other creeping issue. How refreshing would it be to have at least one engine for which continual improvement was practically guaranteed?

Note: at one time I felt that DirectHit was that engine that would continue to improve forever. While it did not use an interrogative method, it was designed to leverage user data. Sadly it did not survive as an independent entity.