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 that they can: Gifts.com, the shopping site that is part of the IAC empire (s iaci), 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 is using Hunch, you are 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 (inclduing those who have birthdays coming up) and clicking on any of them brings up a suggested list of presents, along with the question “would this person like this?” and two buttons 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 they are on Hunch’s website, the questions that the service asks range from the prosaic (does the person tend to vote liberal or conservative, etc.) to the somewhat bizarre — including “would this person think it is 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 that people’s answers to those kinds of questions can indicate where they 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 and websites that are 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.
The most obvious comparison to Hunch’s partnership with Gifts.com is Amazon’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 that 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 at all because the friend’s profile didn’t have enough information about their likes and dislikes.
Dixon says that this is one of the things Hunch’s database of correlations between questions and likes or dislikes tries to do: namely, to fill in the gaps or make connections between the things people like on Facebook and the things they might like elsewhere. And it learns over time — Hunch’s recommendations, for example, seemed fairly wide of the mark for many of my friends, even after I did some training of the algorithm by answering questions, but Dixon said that 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.
“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 — and 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. And maybe some day it will be better than just randomly picking up a pair of cufflinks or slippers for that person on your holiday list.