The Ultimate Challenge For Recommendation Engines | MIT Technology Review

If you share an on-line move account with other people in your household, you probably receive some inappropriate recommendations. That may soon change.

The phrase “People who bought X, also bought Y” has become one of the celebrated monikers of the internet era. This particular form of words comes from recommendation engines that analyse the products you have bought in the past to suggest products you might like in future, usually based on the choices made by other people with similar tastes.

Good recommendation engines can increase sales by several percent. Which is why they have become one of the must-have features for online shops and services.

So it is not hard to understand why there is considerable interest in improving the performance of recommendation engines. Indeed, in 2006, the online movie provider, Netflix, offered a prize of $1 million to anybody who could improve their recommendation algorithm by more than 10 percent. The prize was duly snapped up a mere three years later.

So where might the next improvements come from?

Today, we get an answer of sorts thanks to the work of Amy Zhang at the Massachusetts Institute of Technology in Cambridge and a couple of pals. These guys point out that when it comes to online services such as movie providers, several individuals often share the same account. That means that the choice of movies and the ratings on this account are the combined choices of several different people.

The question they set out to answer is whether it is possible to identify shared accounts simply by studying the ratings associated with it. And if so, how should recommendations be modified in response?

via The Ultimate Challenge For Recommendation Engines | MIT Technology Review.


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