GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior
Proceedings of the 2nd ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative Filtering with Privacy
SP '02 Proceedings of the 2002 IEEE Symposium on Security and Privacy
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
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Detecting profile injection attacks in collaborative filtering: a classification-based approach
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Analysis and detection of segment-focused attacks against collaborative recommendation
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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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In the age of information explosion, recommendation system has been proved effective to cope with information overload in e-commerce area. However, unscrupulous producers shill the systems in many ways to make profit, and it makes the system imprecise and unreliable in a long term. Among many shilling behaviors, a new form of attack, called group shilling, appears and does great harm to the system. Because group shilling users are now well organized and become more hidden among various normal users, it is hard to find them by traditional methods. However, these group shilling users are similar to some extent, for they both shill the target items. We bring out a similarity spreading algorithm to find these group shilling users and protect recommendation system from unfair ratings. In our algorithm, we try to find these cunning group shilling users through propagating similarities from items to users iteratively. The experiment shows our similarity spreading algorithm improves the precision of the system and provides the system a reliable protection.