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Another company, IntroAnalytics, whose engine is used by dating sites like those run by the lad magazines FHM and Maxim, also has applied recommendation technology to online dating. But it differs from Wings in that it uses the data provided by the sites to which it licenses its technology–like profile information and user browsing patterns. IntroAnalytics cofounder Gavin Potter says that this method is very effective. On one site, for instance, Potter found that 60 percent to 70 percent of users’ navigation employed the recommendations provided by his technology instead of the search function, in which people look for traits they think they want.

To be sure, science can’t wave an algorithmic wand to find the perfect mate. Recommendation engines tease out correlations from huge data sets to make smarter suggestions, not perfect predictions. And the data set is never perfect. Nagaraj concedes that Wings could be drawing from more data streams to get a fuller picture of a person, and he is working to integrate more online services with Wings.

One other problem is that people aren’t consistent in their opinions, says Caterina Fake, a cofounder of a Web recommendation engine called Hunch. It tries to tackle questions that Google doesn’t easily answer, such as, “What movie should I see?” Fake gives an example of how people change their minds: “We may predict that you will rate some movie five stars, and right after you watch it, you do give it five stars. But a month later, you might come back and rate it three stars, because it’s faded in memory.”

Still, collecting and analyzing social data the way Wings does could be a new branch in the evolution of Web services that make smarter recommendations without having to be told something twice –or even once.

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Credit: Triangulate

Tagged: Web, social media, analytics, recommendation engine

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