Interactive Intent Modeling: Information Discovery Beyond Search

Interactive Intent Modeling: Information Discovery Beyond Search by Tuukka Ruotsalo, Giulio Jacucci, Petri Myllymäki, Samuel Kaski.

From the post:

Combining intent modeling and visual user interfaces can help users discover novel information and dramatically improve their information-exploration performance.

Current-generation search engines serve billions of requests each day, returning responses to search queries in fractions of a second. They are great tools for checking facts and looking up information for which users can easily create queries (such as “Find the closest restaurants” or “Find reviews of a book”). What search engines are not good at is supporting complex information-exploration and discovery tasks that go beyond simple keyword queries. In information exploration and discovery, often called “exploratory search,” users may have difficulty expressing their information needs, and new search intents may emerge and be discovered only as they learn by reflecting on the acquired information. 8,9,18 This finding roots back to the “vocabulary mismatch problem” 13 that was identified in the 1980s but has remained difficult to tackle in operational information retrieval (IR) systems (see the sidebar “Background”). In essence, the problem refers to human communication behavior in which the humans writing the documents to be retrieved and the humans searching for them are likely to use very different vocabularies to encode and decode their intended meaning. 8,21

Assisting users in the search process is increasingly important, as everyday search behavior ranges from simple look-ups to a spectrum of search tasks 23 in which search behavior is more exploratory and information needs and search intents uncertain and evolving over time.

We introduce interactive intent modeling, an approach promoting resourceful interaction between humans and IR systems to enable information discovery that goes beyond search. It addresses the vocabulary mismatch problem by giving users potential intents to explore, visualizing them as directions in the information space around the user’s present position, and allowing interaction to improve estimates of the user’s search intents.

What!? All those years spend trying to beat users into learning complex search languages were in vain? Say it’s not so!

But, apparently it is so. All of the research on “vocabulary mismatch problem,” “different vocabularies to encode and decode their meaning,” has come back to bite information systems that offer static and author-driven vocabularies.

Users search best, no surprise, through vocabularies they recognize and understand.

I don’t know of any interactive topic maps in the sense used here but that doesn’t mean that someone isn’t working on one.

A shift in this direction could do wonders for the results of searches.

Comments are closed.