Recommendation theory

Devavrat Shah's group at MIT's Laboratory for Information and Decision Systems (LIDS) specializes in analyzing how social networks process information. In 2012, the group demonstrated algorithms that could predict what topics would trend on Twitter up to five hours in advance; this year, they used the same framework to predict fluctuations in the prices of the online currency known as Bitcoin. Next month, at the Conference on Neural Information Processing Systems, they'll present a paper that applies their model to the recommendation engines that are familiar from websites like Amazon and Netflix - with surprising results. "Our interest was, we have a nice model for understanding data-processing from social data," says Shah, the Jamieson Associate Professor of Electrical Engineering and Computer Science. "It makes sense in terms of how people make decisions, exhibit preferences, or take actions. So let's go and exploit it and design a better, simple, basic recommendation algorithm, and it will be something very different. But it turns out that under that model, the standard recommendation algorithm is the right thing to do." The standard algorithm is known as "collaborative filtering." To get a sense of how it works, imagine a movie-streaming service that lets users rate movies they've seen.