Archive for the ‘Intent’ Category

Interactive Intent Modeling: Information Discovery Beyond Search

Wednesday, March 18th, 2015

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.

Intent vs. Inference

Tuesday, May 8th, 2012

Intent vs. Inference by David Loshin.

David writes:

I think that the biggest issue with integrating external data into the organization (especially for business intelligence purposes) is related to the question of data repurposing. It is one thing to consider data sharing for cross-organization business processes (such as brokering transactions between two different trading partners) because those data exchanges are governed by well-defined standards. It is another when your organization is tapping into a data stream created for one purpose to use the data for another purpose, because there are no negotiated standards.

In the best of cases, you are working with some published metadata. In my previous post I referred to the public data at, and those data sets are sometimes accompanied by their data layouts or metadata. In the worst case, you are integrating a data stream with no provided metadata. In both cases, you, as the data consumer, must make some subjective judgments about how that data can be used.

A caution about “intent” or as I knew it, the intentional fallacy in literary criticism. It is popular in some legal circles in the United States as well.

One problem is that there is no common basis for determining authorial intent.

Another problem is that “intent” is often used to privilege one view over others as representing the “intent” of the author. The “original” view is beyond questioning or criticism because it is the “intent” of the original author.

It should come as no surprise that for law (Scalia and the constitution) and the Bible (you pick’em), “original intent” means agrees with the speaker.

It isn’t entirely clear where David is going with this thread but I would simply drop the question of intent and ask two questions:

  1. What is the purpose of this data?
  2. Is the data suited to that purpose?

Where #1 may include what inferences we want to make, etc.

Cuts to the chase as it were.

Serendipity Is Not An Intent

Tuesday, November 15th, 2011

Serendipity Is Not An Intent

From the post:

Wired had two amazing pieces on online advertising yesterday and while Felix Salmon’s piece The Future of Online Advertising could be Yieldbot’s manifesto it is the piece Can ‘Serendipity’ Be a Business Model? that deals more directly with our favorite topic, intent.


Twitter is the greatest discovery engine ever created on the web. But discovery can be and not be serendipitous. Sometimes,, as Dorsey alludes to, you discover things you had no idea existed but much more often you discover things after you have intent around what you want to discover. This is an important differentiation for Twitter to consider. It’s important because it’s a different algorithm.

Discovery intent is not an algo about “how do we introduce you to something that would otherwise be difficult for you to find, but something that you probably have a deep interest in?” There is no “introduce” and “probably” in the discovery intent algo. Most importantly, there is no “we.” It’s an algo about “how do you discover what you’re interested in.”

Discovering more about what you’re interested in has always been Twitter’s greatest strength. It leverages both user-defined inputs and the rich content streams where context and realtime matching can occur. Just like Search.

If Twitter wants to build a discovery system for advertising it should look like this. (emphasis added)

Inverts the advertising and when you think about it, the search algorithm. Rather than discovering, poorly, what interests the user or answer as question, enable the user to discover (a pull model) what interests them.

Completely different way of thinking about advertising and search.

Priesthood of the user? Worked (depending on who you ask) a long time ago.

Maybe, just maybe, a service architecture based on that as a goal, could disrupt the current “I know better than you” push models for search and advertising.