Manipulation-Resistant Recommender Systems

Rahul Sami and Paul Resnick


Online recommender systems are widely deployed as tools to guide users towards items they will like. There is a growing concern that recommender systems may be manipulated by people with a vested interest in having certain items recommended (or not recommended). This is exacerbated as it is often easy for a manipulator to create multiple online accounts to execute an attack. The goal of this project is 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.

Funding support

This project is supported by the National Science Foundation under award IIS-0812042. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Publications (reverse chronological order)


The SimRecommender recommender and attack simulation software we have developed is available through Sourceforge. We will update this as we develop the software further. Please do let us know if you find this useful, by sending email to rsami AT