Sean Peisert

Home Page

Publications

Research Projects

Software

Talks and Tutorials

Professional Service

Teaching

Students & Postdocs

News

Bio

Links


Photograph of me lecturing at the blackboard (credit: R. Benjamin Shapiro, 2002).


Upcoming events that I'm involved with:

S&P 2019 (May 19–23, 2019)

CSET 2019 (August 2019)

NSPW 2019 (Aug/Sept/Oct 2019)

 
 

Research


Recommendation Systems

This project was looking at the security of recommender systems. It evaluated vulnerabilities in relatively unstudied model-based recommendation systems in which recommendations are based on a model that relates ratings on one item to ratings on other items. Recommendation systems are a means of reducing "information overload" by filtering a potentially overwhelming number of options (such as all the products available from a seller) to identify those calculated to be of greatest interest. This project extends research on collaborative recommendation systems, which base recommendations for an individual on the preferences expressed by other people, by investigating the problem of malicious manipulation of these systems, for example, by an attacker attempting to influence the outcome with biased or faked rating profiles. Research suggests that a specific model-based systems exhibit much more resistant to recommendation attacks than memory-based systems in which recommendations are based on the principle of finding similar users or similar items. Moreover, this research investigated previously unknown attack methods that might be specifically effective against model-based recommendation systems.

Sponsor: National Science Foundation

Researchers involved:

Faculty: Graduate Students and Alumni:
  • Bhume Bhumitarana (UC Davis, Ph.D. 2009; → Faculty at Univ. of Technology, Thonburi, Thailand)
  • Michael Clifford (UC Davis)
  • Steven Templeton (UC Davis)

The definitive versions of the papers posted on this page were first published in the venues indicated. In accordance with publisher copyright policies, these papers are pre-prints or post-prints, and are not the pubilsher's version.

Personal use of the material posted on this page is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the original publishers.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.


Last modified: Sunday, 20-Oct-2013 19:38:03 PDT