Catching Up With Hunch

Last week, I stopped by the Hunch office to learn more about what they’re doing, as well as to contribute my own thoughts about socially enhanced decision making. I consider Hunch, like Aardvark, to be an example of social search, but I recognize that I use the term in a broad sense. Perhaps, as Jeremy suggests, it’s better to think of social search and collaborative search being different aspects of multi-person search.

In any case, Hunch is doing some interesting things. Their mission, roughly speaking, is to become a Wikipedia for decision making. They are inspired by human computation success stories like 20Q.net and presumably the ESP Game. Their general approach is to learn about people by asking them multiple-choice questions that help cluster them demographically (”Teach Hunch About You”), and then to create customized decision trees to help people find their own answers to questions. The questions themselves are crowd-sourced from users (though now they are vetted first in a “workshop”).

They’re learning as they go along. For example, they’ve recognized that it’s important to distinguish between objective questions (e.g., concerning the price of a product) and…

Last week, I stopped by the Hunch office to learn more about what they’re doing, as well as to contribute my own thoughts about socially enhanced decision making. I consider Hunch, like Aardvark, to be an example of social search, but I recognize that I use the term in a broad sense. Perhaps, as Jeremy suggests, it’s better to think of social search and collaborative search being different aspects of multi-person search.

In any case, Hunch is doing some interesting things. Their mission, roughly speaking, is to become a Wikipedia for decision making. They are inspired by human computation success stories like 20Q.net and presumably the ESP Game. Their general approach is to learn about people by asking them multiple-choice questions that help cluster them demographically (”Teach Hunch About You”), and then to create customized decision trees to help people find their own answers to questions. The questions themselves are crowd-sourced from users (though now they are vetted first in a “workshop”).

They’re learning as they go along. For example, they’ve recognized that it’s important to distinguish between objective questions (e.g., concerning the price of a product) and questions of taste (e.g., what is art?). They’re also experimenting with interface tweaks, including giving users more control over what information their algorithms use to rank potential answers, and allowing users to short-circuit the decision tree at any time by skipping to the end.

Perhaps of particular interest to readers here, they’ve made an API available, which you can also play with in a widget on their blog.

As I told my friend at Hunch, I’m still skeptical about decision trees. Maybe I’m a bit too biased toward faceted search, but I don’t like having such a rigid decision making process. Apparently they’re not wedded to decision trees, but they are understandably concerned about creating a richer interface that might turn off or  intimidates ordinary users. I can’t deny that decision trees are simple to use, and I can’t argue with their 77% success rate.

Still, the rigidity of a decision tree leaves me a bit cold. Even if it leads me to the right choice, it doesn’t give me the necessary faith in that choice. Transparency helps, and I like that you can click on “Why did Hunch pick this?” to see what in your question-specific or personal profile led Hunch to recommend that answer. But I’d like more freedom and less hand-holding.

I still have a handful of invites; let me know if you’re interested. As usual, first come, first serve.

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