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TrapIt’s founders argue that this approach provides the perfect balance of serendipity and precision. While search engines recommend popular Web pages for a particular topic, TrapIt is designed to do a better job of surfacing obscure content, Griffiths says. And while social media can provide interesting new links, TrapIt can draw on more content and make sure recommendations stay closely related to a user’s interests.

The company has also put a lot of effort into packaging the site. For every trap, the company’s algorithms extract headlines and images.

TrapIt’s team hopes that users will not only enjoy the site but also understand that they are benefiting from heavyweight technology. A lot of recommendation engines are very manually driven, says Frank Meehan, CEO of the social mobile company INQ and a member of TrapIt’s board of directors. For example, Facebook’s “Like” button relies on user clicks to gain information about people’s tastes. Crucially, in Meehan’s view, TrapIt’s design reveals the considerable artificial intelligence beneath the interface. Showing featured and trending traps will reveal how well the algorithms select content, he says.

TrapIt’s founders envision supporting the service with advertising, but they also have other ideas for how it might make money. For example, they could offer paid premium service to users who want to research niche topics such as law or biomedicine; this could include access to content normally hidden behind pay walls. The company also hopes to license the platform to media companies, who could use the technology to put together personalized packages of content for subscribers and other users.

For now, however, Griffiths says the company is just focused on making the site work well. In early tests, users averaged 18 page views per visit—an unusually high number. (Facebook, an acknowledged leader in this area, averages about 24 page views per visit.) Griffiths says he hopes the site’s ability to capture people’s attention will build a strong user base and attract advertisers.

Daniel Tunkelang, principal data scientist at LinkedIn and an expert in information retrieval, says personalization through data analysis can be complementary to personalization through social analysis. He notes that researchers have long considered developing recommender systems that use both social- and content-based methods to make recommendations.

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Credit: TrapIt

Tagged: Web, search, startups, social media, artificial intelligence, personalization, recommendation engines

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