How to devise the perfect recommendation algorithm

AT LAST YEAR’S consumer-electronics show in Las Vegas, Reed Hastings, the CEO of Netflix, set out an ambitious goal for serving his customers: “One day we hope to get so good at suggestions that we’re able to show you exactly the right film or TV show for your mood when you turn on Netflix.”

But what is exactly the right show? Mr Hastings’s company has been a pioneer in the science of recommendation algorithms, dating back to its days as a humble DVD-by-mail company. Netflix’s thinly sliced classifications of films and TV shows, and its equally finely graded assessments of customers’ viewing preferences, established the standard for product suggestions.

Still, algorithms take some of the adventure and serendipity out of hunting for new entertainment, and rarely nudge a customer towards anything way off his radar. This is a challenge for independent producers of music, literature or film, who already find it extremely difficult to get noticed amidst so much choice. Recommendation software can make the problem worse.

Suggestion algorithms can exploit what customers are known to like by pushing similar fare, or they can encourage...