New Toys from Hunch

I’ve been following Hunch for a while, and my impression has evolved from the initial skepticism with which I greeted it a year ago (to the day!). Given the track records of co-founders Caterina Fake and Chris Dixon, perhaps I should have expected their success at obtaining traffic and funding.  But what interests me more is that they are doing interesting things with data mining and putting a new twist on social media analytics.

For those unfamiliar with Hunch, it is a decision engine (cf. [real decision engine] vs. [decision engine]). For example, it can help you decide whether to buy an iPad or how to name your baby. While it’s not clear to me how much people are using Hunch for utility vs. entertainment, Hunch is certainly accumulating users–as well as the data that those users volunteer.

Hunch recently released two applications that mash up that data with the Twitter follower graph. The first is a “Twitter Predictor Game” that attempts to calculate your taste profile from your Twitter id and then predict how you’ll answer Hunch’s taste questions. Just to keep the game honest, you can look at the Hunch’s guess either before or after you provide your answer. The second is called “Twitter Follower Stats“: given a Twitter user, it reports the salient information it has inferred about that user’s followers (e.g., @maddow vs. @karlrove).

I think this stuff is neat, and a great testament to the “unreasonable effectiveness of data“. The question-answer data still feels a bit sparse for my taste, and I suspect there’s still room for more dimensionality reduction. I’m sure Hunch CTO Matt Gattis and colleagues are working on it! Also, it would be neat to direct the follower analytics rather than simply see the ones that Hunch deems most salient.

In summary, Hunch is keeping it interesting. Definitely a startup to watch and learn from.

By Daniel Tunkelang

High-Class Consultant.

3 replies on “New Toys from Hunch”

Hey Daniel – Thanks for the kind words…

I am clearly a big believer in the effectiveness of big data. I guess you could say I’m betting my career on it 🙂

Besides the usual iterative product development process making Hunch better, then main difference between now and launch is that we didn’t have much data at all back then. We had a massive chicken-and-egg problem. Now we have the egg (or chicken) at least.

The stuff we’ve released lately are mainly “stunts” that use our new data but soon we will be releasing more practical stuff that focuses on using the “taste graph” we’ve been building. Will certainly be looking forward to your feedback on this stuff.


Indeed, I’m impressed that you got over the hurdle of collecting that critical mass of data. Looking forward to seeing what you do with it, and always happy to offer feedback.


Hunch is interesting as it appears to be building a web-wide recommendation engine ie. offering automatic predictions about the interests of a user from the continual collection of taste information (“taste-graph”) from many users. Current recommendation engines work to answer “people who liked your choice also liked these”. Popular examples are Amazon, iTunes and Netflix. We took a different approach that focuses on “suggestions” to answer “if you liked this you will also like these” which is about understanding the users mindset. We applied our approach successfully to both music and movie data with results that were personal to the user. Overall, it is a fascinating area.


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