GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
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
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Shifting and scaling patterns from gene expression data
Bioinformatics
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)
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Proceedings of the third ACM conference on Recommender systems
Donation dashboard: a recommender system for donation portfolios
Proceedings of the third ACM conference on Recommender systems
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
A Case Study of Collaboration and Reputation in Social Web Search
ACM Transactions on Intelligent Systems and Technology (TIST)
A hybrid decision approach to detect profile injection attacks in collaborative recommender systems
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Defending recommender systems by influence analysis
Information Retrieval
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Trust, reputation and recommendation are key components of successful e-commerce systems. However, e-commerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation and recommendation. One such vulnerability is the use of fraudulent user profiles to boost (or damage) the ratings of items in an online recommender system. In this paper we cast this problem as a problem of detecting anomalous structure in network analysis and propose a novel mechanism for detecting this anomalous structure. We present an evaluation that shows that this approach is effective at uncovering the types of recommender systems attack described in the literature.