Manipulation-Resistant Recommender Systems

Rahul Sami and Paul Resnick

Overview

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)

A Prediction Market Approach to Learning with Sequential Advice
Sindhu Kutty and Rahul Sami
2010 Workshop on Computational Social Science and the Wisdom of Crowds [PDF]
The Copied-Item Injection Attack
Nathan Oostendorp and Rahul Sami
2009 Workshop on Recommender Systems and the Social Web [PDF]
Assessment of Conversation Co‐mentions as a Resource for Software Module Recommendation
Daniel Xiaodan Zhou and Paul Resnick
Proceedings of the ACM Recommender Systems Conference, 2009
Sybilproof Transitive Trust Protocols
Paul Resnick and Rahul Sami
Proceedings of the 2009 ACM Electronic Commerce Conference (2009) [PDF]
Manipulation-resistant recommender systems through influence limits
Paul Resnick and Rahul Sami
Overview article in ACM SIGECOM Exchanges, Vol 7, Number 3.
The Information Cost of Manipulation Resistance in Recommender Systems
Paul Resnick and Rahul Sami
Proceedings of the ACM Recommender Systems Conference, 2008 . [PDF]
The Influence-Limiter: Provably Manipulation-Resistant Recommender Systems
Paul Resnick and Rahul Sami
Proceedings of the ACM Recommender Systems Conference, 2007. [PDF]

Software

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 umich.edu.