2009 Poster Sessions : Manipulation Robust Collaborative Filtering Systems

Student Name : Robbie Yan
Advisor : Benjamin Van Roy
Research Areas: Information Systems
Abstract:
A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, a class of probabilistic inference algorithms which we refer to as linear is relatively robust. These results provide guidance for the design of future collaborative filtering systems.


Bio:
Robbie Yan is a PhD student at Stanford. His research interests include topics in information technology, finance, and optimization. He has previously interned at Google and Morgan Stanley.