Elements of information theory
Elements of information theory
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Preventing shilling attacks in online recommender systems
Proceedings of the 7th annual ACM international workshop on Web information and data management
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Prediction, Learning, and Games
Prediction, Learning, and Games
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Robustness of collaborative recommendation based on association rule mining
Proceedings of the 2007 ACM conference on Recommender systems
Attack resistant collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Trust no one: evaluating trust-based filtering for recommenders
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Competitive collaborative learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Manipulation-resistant recommender systems through influence limits
ACM SIGecom Exchanges
Sybilproof transitive trust protocols
Proceedings of the 10th ACM conference on Electronic commerce
Donation dashboard: a recommender system for donation portfolios
Proceedings of the third ACM conference on Recommender systems
Rating aggregation in collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
The "top N" news recommender: count distortion and manipulation resistance
Proceedings of the fifth ACM conference on Recommender systems
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Computers in Human Behavior
Macau: a basis for evaluating reputation systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Attackers may seek to manipulate recommender systems in order to promote or suppress certain items. Existing defenses based on analysis of ratings also discard useful information from honest raters. In this paper, we show that this is unavoidable and provide a lower bound on how much information must be discarded. We use an information-theoretic framework to exhibit a fundamental tradeoff between manipulation-resistance and optimal use of genuine ratings in recommender systems. We define a recommender system to be (n, c)-robust if an attacker with n sybil identities cannot cause more than a limited amount c units of damage to predictions. We prove that any robust recommender system must also discard Ω(log (n/c)) units of useful information from each genuine rater.