Rahul Sami

E-commerce is just one example of how recommender systems are used online, but what if someone was manipulating the system to steer customers to certain products over others?

Assistant Professor Rahul Sami wants to guard against that. His study, "Manipulation-Resistant Recommender Systems," funded by the National Science Foundation. Sami works with Professor Paul Resnick on the project.

The project seeks to develop general techniques for the design of manipulation-resistant recommender systems, as well as specific solutions for applications in which such a recommender could have a significant impact.

One class of applications that will be studied involves Internet sites that use user-provided ratings or tags to recommend books, images, sites, or other products to users.

Another application the researchers are focusing on is the design of a recommendation system for job candidates that aggregates informal information from former employees and colleagues.

You can read more about this project.