Recommendation systems should instead ask users to compare products in pairs, not as stand-alone items, says Devavrat Shah, a professor at MIT’s Laboratory of Information and Decisions Systems. According to Shah, the kind of star rating systems that are the status quo on the web today are flawed because, well, humans are flawed. “If my mood is bad today, I might give four stars, but tomorrow I’d give five stars. But if you ask me to compare two movies, most likely I will remain true to that for a while,” Shah says in an article published this week on MIT’s news site. “Your three stars might be my five stars, or vice versa. For that reason, I strongly believe that comparison is the right way to capture this.” In a series of recently published academic papers, Shah, along with students Ammar Ammar and Srikanth Jagabathula, as well as MIT Sloan School of Management professor Vivek Farias, demonstrated that stitching “pairwise rankings” together into a master list is a more accurate representation of customer sentiment than relying on customers to rate things by themselves on a typical five-star scale. According to the MIT researchers, they have formulated algorithms that have proven to accurately predict shoppers’ preferences with 20 percent greater accuracy than the kinds of formulas most often in use today. They have built a website, Celect.com, to show off their theories in practice. Get the full story at GigaOM