The blending of different approaches has gotten a lot of attention in post-mortems of the competition, but John Riedl, a professor of computer science at the University of Minnesota, says he has mixed feelings about it. “People like me have been looking for ideas that would give us insight into the structure of the solution,” he says, “where we really understand something new about not only what solution does well, but why it is that it does well.”
The winning models, however, haven’t yielded such insight. What they do suggest, according to Riedl, is that combining lots of algorithms with machine-learning techniques might be a good approach to handling large datasets in general. However, even that remains to be proven. “A lot of us are worried that this approach may not be as fruitful elsewhere,” he adds.
What is clear is that many industries could benefit from the types of models built for the competition. Besides other online recommendation systems, Ampazis suggests that such algorithms could be applied in market trading, fraud detection, spam-fighting, and computer security. Bertino says that members of The Ensemble are currently considering how best to use the technology they generated in the course of the competition.
Potter is working on applying his own research for the prize to the online dating site YesNoMayB, which employs two-way recommendation algorithms to find users who may want to meet one another. In particular, he hopes to use insights from the Netflix Prize to make predictions based on users’ implicit preferences, such as what pages they load.
The Netflix Prize focused a lot of attention on recommendation systems and produced huge advances in the field. The second competition seems likely to do the same. But Riedl thinks that other components of recommendation systems may be left behind in the process. “Now it’s time for us as a field to think about what other aspects may have been neglected,” he says, “and how researchers can make progress on those aspects in a way that has implications for industry.”
For example, Riedl sees a need for algorithms that allow recommendation systems to use ever-larger sets of data, systems that explain to a user why a particular recommendation was made, and better user interfaces. He also notes that, while the Netflix competition made impressive advances in interpreting sparse data, in some cases it may make sense to learn how to design sites to encourage users to give more data. He hopes that the upcoming meeting in New York will help define a broader set of questions for researchers to address.