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ABSTRACT

In this letter, we outline a new approach to modeling, analyzing, and combating manipulative attacks on recommender systems.

REFERENCES

• 1: Rajat Bhattacharjee , Ashish Goel, Algorithms and incentives for robust ranking, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, p.425-433, January 07-09, 2007, New Orleans, Louisiana
• 2: Nicolo Cesa-Bianchi , Gabor Lugosi, Prediction, Learning, and Games, Cambridge University Press, New York, NY, 2006
• 3: Paul-Alexandru Chirita , Wolfgang Nejdl , Cristian Zamfir, Preventing shilling attacks in online recommender systems, Proceedings of the 7th annual ACM international workshop on Web information and data management, November 04-04, 2005, Bremen, Germany [doi>10.1145/1097047.1097061]
• 4: CONITZER, V. 2008. Anonymity-proof voting rules. In Proceedings of the Fourth Workshop on Internet and Network Economics (WINE'08).
• 5: Shyong K. Lam , John Riedl, Shilling recommender systems for fun and profit, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988726]
• 6: Bhaskar Mehta , Thomas Hofmann , Peter Fankhauser, Lies and propaganda: detecting spam users in collaborative filtering, Proceedings of the 12th international conference on Intelligent user interfaces, January 28-31, 2007, Honolulu, Hawaii, USA [doi>10.1145/1216295.1216307]
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• 8: Bamshad Mobasher , Robin Burke , Runa Bhaumik , Chad Williams, Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness, ACM Transactions on Internet Technology (TOIT), v.7 n.4, p.23-es, October 2007 [doi>10.1145/1278366.1278372]
• 9: NEWITZ, A. 2007. I bought votes on digg. Wired Magazine. Available at http://www.wired.com/techbiz/people/news/2007/03/72832.
• 10: Michael P. O'Mahony , Neil Hurley , Guenole C. M. Silvestre, Promoting Recommendations: An Attack on Collaborative Filtering, Proceedings of the 13th International Conference on Database and Expert Systems Applications, p.494-503, September 02-06, 2002
• 11: Michael P. O'Mahony , Neil J. Hurley , Guénolé C.M. Silvestre, Detecting noise in recommender system databases, Proceedings of the 11th international conference on Intelligent user interfaces, January 29-February 01, 2006, Sydney, Australia [doi>10.1145/1111449.1111477]
• 12: Paul Resnick , Rahul Sami, The influence limiter: provably manipulation-resistant recommender systems, Proceedings of the 2007 ACM conference on Recommender systems, October 19-20, 2007, Minneapolis, MN, USA [doi>10.1145/1297231.1297236]
• 13: Paul Resnick , Rahul Sami, The information cost of manipulation-resistance in recommender systems, Proceedings of the 2008 ACM conference on Recommender systems, October 23-25, 2008, Lausanne, Switzerland [doi>10.1145/1454008.1454033]
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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.4 INFORMATION SYSTEMS APPLICATIONS
      H.4.2 Types of Systems
          Subjects: Decision support (e.g., MIS)

Additional Classification:
  H. Information Systems
  H.1 MODELS AND PRINCIPLES
      H.1.1 Systems and Information Theory
          Subjects: Value of information
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

General Terms:
Algorithms, Design, Management, Theory

Keywords:
information loss, manipulation-resistance, recommender systems, shilling