Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
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)
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Robust collaborative filtering
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
Effective diverse and obfuscated attacks on model-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Manipulation-resistant collaborative filtering systems
Proceedings of the third ACM conference on Recommender systems
Robustness analysis of model-based collaborative filtering systems
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
βP: A novel approach to filter out malicious rating profiles from recommender systems
Decision Support Systems
Multi-source deep learning for information trustworthiness estimation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Collaborative Filtering systems are essentially social systems which base their recommendation on the judgment of a large number of people. However, like other social systems, they are also vulnerable to manipulation. Lies and Propaganda may be spread by malicious users who may have an interest in promoting an item, or downplaying the popularity of another one. By doing this systematically, with either multiple identities, or by involving more people, malicious shilling user profiles can be injected into a collaborative recommender system which can significantly affect the robustness of a recommender system. While current detection algorithms are able to use certain characteristics of shilling profiles to detect them, they suffer from low precision, and require a large amount of training data. The aim of this work is to explore simpler unsupervised alternatives which exploit the nature of shilling profiles, and can be easily plugged into collaborative filtering framework to add robustness. Two statistical methods are developed and experimentally shown to provide high accuracy in shilling attack detection.