About Hunch

Hunch is developing an online decision engine. The company was also known as DGP Labs. Hunch aims to, in 10 questions or less, offer users a great recommendation to address their choice, problem, or dilemma, on thousands of topics. Hunch's recommendations are based on the collective knowledge of the entire Hunch community, narrowed down to people like the user, or just enough like the user. Hunch is designed so that every time it's used, it learns something new. That means, per the company, that Hunch's hunches are always getting better. At the core of Hunch is a question selection algorithm. The algorithm is always asking itself, "What can I ask you next which will lead to the best possible recommendation for this topic" The choice of which questions to ask and when to ask them will vary based on what the user has already been asked (and how the user answered) so far, the same way that a human expert would adjust a line of questioning based on your responses. In choosing what to ask users, Hunch's question selection algorithm tries to do two things. First, it tries to find a question which will discriminate well among the remaining possible recommendation outcomes for the user - thus filtering the remaining choices from "many" to "fewer". Second, the algorithm looks for a question which can help optimize and rank the remaining recommendation outcomes to present users with the ones they'll like the most. It's trying to ensure that the users will like outcome #1 better than outcome #5. As users answer questions, Hunch can narrow down possible recommendations outcomes because each outcome can be "trained" to correspond with each question's answers. Any logged in user can set initial training or correct existing training, in addition to proposing new topics, questions to ask, and recommendation outcomes. Hunch also uses machine learning based on statistical inferences. When a user clicks "Yes" or "No" to indicate whether or not they like one of Hunch's recommendations, Hunch incrementally strengthens or weakens the mathematical correlation between that recommendation and any 'Teach Hunch About You' questions that have been answered so far. So over time, Hunch might hypothetically learn that people living in cities tend to prefer diet sodas, or that SCUBA divers tend to like bicycles with lots of gears. These type of algorithms are called machine learning. In November 2011, Hunch was acquired by eBay for an acquisition price / valuation rumored to be $80 million.