Hunch Inc. Fact Sheet

Thanks for your interest in Hunch!

Updated September 13, 2011

What is Hunch?

Hunch helps you make and discover great recommendations that are customized to your tastes. The company's mission is to build a 'Taste Graph' connecting every person on the web with their affinity for every entity (camera, car, book, anything!) on the web.

Hunch proposes concrete and customized recommendations for many different topics. Hunch customizes its results by asking you questions about who you are and then recommending things that are liked by people similar to you.

Hunch uses machine learning to get smarter in two ways:

  • User contributions train Hunch to be smarter overall. Contributions can take many forms, from rating results to adding a descriptive tag to a recommendation, to suggesting new recommendations that other people might like.
  • The more Hunch learns about each individual user's personality and preferences, the better Hunch can customize recommendations for that user. It's like a friend getting to know someone's taste and preferences over time, so they can provide sound and trusted advice.

What problem does Hunch solve?

Our goal is for a user to be able to come to Hunch and have fun making and discovering recommendations that are smart and tailored to each individual.

Eventually, when Hunch gets good enough, we hope users will trust its recommendations without having to turn to lots of external time-consuming sources of information.

What stage is Hunch in?

Hunch opened to the public on June 15, 2009.

Hunch can make predictions about more than 500 million people based on information in the Taste Graph.

  • As more people use Hunch, it will become both smarter about what it knows and broader in the scope of recommendations it makes.

How does Hunch make money?

  • Some of the recommendation pages on Hunch link to external sites where users can purchase the product or service that Hunch proposed. If they do, Hunch may earn a referral fee from the merchant.
  • The presence of a link to a retailer has no effect on the recommendation which Hunch proposes. Within a given topic, it's likely that some recommendation pages will link to an online retailer, and others won't. Some topics don't have these sorts of links at all.
  • Approved external companies may draw on Hunch's Taste Graph to improve the recommendations on their own sites. Hunch may charge for these services.

How is Hunch different from other sites?

  • Hunch proposes customized recommendations of every kind.
  • Hunch gets smarter by harnessing the collective knowledge of the entire Hunch community.

What's the history of Hunch and who is behind it?

  • Hunch is based in New York City.
  • The core algorithm was built by MIT computer scientists with backgrounds in machine learning.
  • Hunch began a preview to the public on March 27, 2009, and opened fully to the public on June 15, 2009.

Contact for additional questions

Kelly Ford
VP of Marketing