Today’s Baltimore Sun has a front page story (Yes, in fact, there is accounting for taste) on recommendation systems that are critical for many online retailers to predict consumer’s interests. The article mentions the research of two UMBC Ph.D. students, Marc Pickett and Sandor Dornbush.
Marc’s applying ideas from his dissertation to try to solve the Netflix challenge and win a million dollars.
Marc Pickett wants to take luck out of that equation. And win a million dollars in the process. Pickett, a doctoral student at the University of Maryland, Baltimore County, is trying to perfect a “recommender.” That’s a computer program designed to analyze your cinematic tastes and predict what movies you’ll like. … The program is particularly critical for Netflix, an online mail-order movie-rental giant whose livelihood depends on keeping customers happy enough to pay $5.99 or more every month for the opportunity to watch its videos. In October, the company offered a $1 million prize to anyone who could develop a program 10 percent more accurate than its current recommender, known as Cinematch. A chance at that chunk of change set thousands of programmers around the country, including Pickett, to work on the problem.
Sandor has been developing an idea that originally came out of a group project done as part of Professor Zary Segall’s wearable computing class.
Too busy to tell a recommender what you’d like to hear? Sandor Dornbush, another UMBC graduate engineering student, is working on a mood-sensing MP3 player to free you from that task. The portable player, for now dubbed the XPod, will monitor physiological signs such as heartbeat and skin temperature to determine what kind of music to play, Dornbush said. “It could find, for example, that when you run you like upbeat music,” he said. But like all recommenders, human or digital, Dornbush’s prototype needs to spend time with a person to get to know him. “If you don’t give them any feedback,” Dornbush said, “they have a tough time figuring out what you like.”