Gigaom

Can Hunch's Algorithm Improve Your Gift-Giving Skills?


With the holidays approaching, we're entering that time of year when desperate people grab things like ties, inappropriate books, and goofy toys in a pathetic attempt to bring joy to their friends and loved ones. Can algorithms help? New York-based startup Hunch thinks they can, and Gifts.com, the shopping site that is part of the IAC empire, apparently agrees. Gifts.com has been using Hunch's recommendation engine for almost two weeks now, and the company says its conversion rate (i.e., the number of people who go from being shoppers to buyers) is as much as 60 percent higher than it was before the site started using the tool to make recommendations.

When you go to the Gifts.com section that's using Hunch, you're prompted to log in with your Facebook account, since Hunch uses your Facebook "social graph," or friend connections, to power the feature. When you log in, you see a list of your friends (including those who have birthdays coming up). Clicking on any of them brings up a suggested list of presents, along with the question "Would this person like this?" and a button for yes and no. You can click those buttons to refine the search, or you can answer some Hunch questions to fine-tune the profile of that person.

Just as on Hunch's website, the service asks questions ranging from the prosaic (does the person tend to vote liberal or conservative, etc.) to the somewhat bizarre—including "Would this person think it's wrong to train dolphins and keep them in captivity for aquatic shows?" and "Would this person believe that alien abductions are real or fake?" Hunch founder Chris Dixon says answers to those kinds of questions can indicate where people fall on other spectrums of likes and dislikes, and can be used to make guesses about what they might be interested in.

The alien abduction question, for example, "tends to correlate highly with political orientation," Dixon says, and the answers that you provide to those kinds of questions allow Hunch to make "cross-category inferences" about whether you might like a certain sports team or whether you like Italian food better than French cuisine. Gifts.com is one of the companies taking part in the rollout of Hunch's recommendation platform, along with ShopStyle, Milo, and FanBridge—and Hunch has also been talking to other companies, including some of the major cellular carriers.

Amazon Comparison

The most obvious comparison to Hunch's partnership with Gifts.com is Amazon.com's gift-suggestion service, which also looks at your Facebook social graph in order to make recommendations—something Amazon launched in July. I haven't used Amazon's feature that much, but after trying it out a few times, I found it was just barely better than a guess or a random pick from one of the site's top most-recommended items. In many cases it didn't do any personalizing, because the friend's profile didn't have enough information about their likes and dislikes.

Dixon says this is one of the things Hunch's database of correlations tries to do: namely, fill in the gaps or make connections between the things people like on Facebook and the things they might like elsewhere. It learns over time—Hunch's recommendations, for example, seemed fairly off the mark for many of my friends, even after I did some training of the algorithm by answering questions, but Dixon said the overlap between the kinds of products at Gifts.com and the kind in Hunch's database is not very large, and so the system is still learning to make connections between the two.

"I think it probably needs another two weeks or so before it becomes mature," Dixon said. The good part of such relationships for Hunch, he says, is that the more data it pulls in about what people like or don't like—the average user at Gifts.com is giving feedback on 20 items, which is higher than expected—the better the system gets at making recommendations. Maybe someday it will be better than just randomly picking up a pair of cuff links or slippers for that person on your holiday list.

Also from GigaOM:

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