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I love books, I like music, and I don’t mind the news. When I’m sent a link to something a friend thinks I should read, hear, or view, I take it seriously. Recommendations are essential to my quality of life.

It’s a good thing I feel this way, because recommendations are everywhere on the Internet. Wherever I shop online, some sliver of my screen is prompting me with a come-hither like “Customers who bought this item also … .” Pop-ups and context-sensitive advertisements have been supplemented by this low, seductive whisper of automated suggestion. The truth is that I now get more good recommendations about more things, more often, from Bayesian algorithms than from my best friends. Perhaps this should make me wistful, but it doesn’t. Better tech­nology doesn’t mean worse friends.

Unlike human recommenders,, ­, and never insult me by implying that I spend my time, money, or attention on the wrong things. They’re simply making relevant–and occasionally novel–recommendations based on my past choices and the things I attend to in real time. The focus of digital personalization has shifted from what I am interested in now to what I might be interested in next. All the choices I make in the moment are absorbed into a sphere of suggestion where, after they have been statistically weighted, they are reborn as offers and advice.

Increasingly, I find myself as curious about a site’s recommendations as about what it sells. That a site is trying to sell me something else seldom annoys me. On the contrary, I like it that Internet companies have dedicated such ingenuity, memory, and processing power to offering me good suggestions. But “good” needs to get much better if recommendations are to expand beyond telling me what I might like right now.

Consider Amazon, whose site displays some of the irksome limitations of current recommendation engines. The company has been a pioneer in this technology since shortly after its launch in 1995. Greg ­Linden, who is now with Microsoft, helped write Amazon’s first recommendation engine, Instant Recommendations, which succeeded where an older system called BookMatcher had failed. The engine evolved incrementally. “We learned what worked and what didn’t by seeing how changes in the recommendations helped people find new books,” Linden says. “We enjoyed helping people discover books they probably would not have found on their own. It was never about marketing–just matching people to books they would love. But it turns out people do buy more when you help them find what they need.”

Today, Amazon makes recommendations on the basis of a customer’s browsing and buying history, other items viewed or purchased by customers who’ve bought the product being viewed, and items that seem related to that product. On Amazon, reviews, recommendations, and rankings become an essential part of browsing and shopping. For example, while I was checking out Predictably Irrational, Daniel Ariely’s new book about apparently dysfunctional decision making, the “Customers Who Bought This Item Also Bought …” strip tipped me off to a forthcoming title I had never heard of: Nudge, by the University of Chicago behavioral economist Richard Thaler and the University of Chicago law professor Cass Sunstein. Click, and I’m there. It’s precisely the sort of real-time connection that makes Amazon shopping superior to both in-person and online alternatives.

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Credit: Marc Rosenthal

Tagged: Web, Google, Apple, Internet, Amazon, recommendation engines

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