Improving recommendation systems
Recommendation algorithms are a vital part of today’s Web, the basis of the targeted advertisements that account for most commercial sites’ revenues and of services such as Pandora, the Internet radio site that tailors song selections to listeners’ declared preferences. The DVD rental site Netflix deemed its recommendation algorithms important enough that it offered a million-dollar prize to anyone who could improve their predictions by 10 percent.But Devavrat Shah, the Jamieson Career Development Associate Professor of Electrical Engineering and Computer Science in MIT’s Laboratory of Information and Decisions Systems, thinks that the most common approach to recommendation systems is fundamentally flawed. Shah believes that, instead of asking users to rate products on, say, a five-star scale, as Netflix and Amazon do, recommendation systems should ask users to compare products in pairs. Stitching the pairwise rankings into a master list, Shah argues, will offer a more accurate representation of consumers’ preferences.In...