This is a joint SENSE/IAM seminar.
The openness and anonymity of the Internet environment create many hazards for e-commerce. For collaborative recommender systems, it raises the possibility of that attackers will seek to bias the output recommendations through manipulation of the public inputs that the system permits. Fighting such manipulation is a constant battle for the owners and maintainers of such systems. In this talk, I will describe the known vulnerabilities of collaborative algorithms and examine a range of possible attack types that could be deployed against them. With these vulnerabilities in mind, I will discuss possible responses, including the deployment of alternate recommendation algorithms and the use of supervised and unsupervised techniques to detect attacks. Building on this research, I will examine what it might mean to build a robust collaborative recommender and consider the implications for other machine learning techniques deployed in public on-line environments.
Dr Robin Burke
Robin Burke is a 2008-2009 Fulbright Scholar at University College Dublin and an associate professor at the School of Computing and Digital Media at DePaul University in Chicago, Illinois. He also held positions at the University of Chicago, the University of California, Irvine, and California State University, Fullerton after receiving his PhD from Northwestern University's Institute for the Learning Sciences in 1993. Dr. Burke has been active in recommender systems research since 1995, and was a pioneer in the area of case-based approaches to knowledge-based recommender systems. His current research examines security properties of recommendation algorithms and alternative evaluation methodologies for recommender systems.