William F. Garrity

Michael Schrage in the May/June 2008 issue of MIT's Technology Review publishes a piece about sites making "recommendations" to users, in shopping and otherwise.  Libraries should similarly recommend information resources to our clients. 

Recommendation Nation

Learning to love customers like you.

By Michael Schrage

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, Apple.com, ­Amazon.com, and Google.com 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.

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This post was brought to my attention by Tom M., a colleague in the Biomedical Libraries.