GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Promoting Recommendations: An Attack on Collaborative Filtering
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
IEEE Transactions on Knowledge and Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
Detecting Profile Injection Attacks in Collaborative Recommender Systems
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
A survey of trust and reputation systems for online service provision
Decision Support Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Robustness of collaborative recommendation based on association rule mining
Proceedings of the 2007 ACM conference on Recommender systems
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Unsupervised shilling detection for collaborative filtering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Proceedings of the third ACM conference on Recommender systems
Simulating the effect of reputation systems on E-markets
iTrust'03 Proceedings of the 1st international conference on Trust management
Error-based collaborative filtering algorithm for top-N recommendation
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Fraud detection in online consumer reviews
Decision Support Systems
Manipulation in digital word-of-mouth: A reality check for book reviews
Decision Support Systems
Manipulation of online reviews: An analysis of ratings, readability, and sentiments
Decision Support Systems
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Input online review data and related bias in recommender systems
Decision Support Systems
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Recommender systems are widely deployed to provide user purchasing suggestion on eCommerce websites. The technology that has been adopted by most recommender systems is collaborative filtering. However, with the open nature of collaborative filtering recommender systems, they suffer significant vulnerabilities from being attacked by malicious raters, who inject profiles consisting of biased ratings. In recent years, several attack detection algorithms have been proposed to handle the issue. Unfortunately, their applications are restricted by various constraints. PCA-based methods while having good performance on paper, still suffer from missing values that plague most user-item matrixes. Classification-based methods require balanced numbers of attacks and normal profiles to train the classifiers. The detector based on SPC (Statistical Process Control) assumes that the rating probability distribution for each item is known in advance. In this research, Beta-Protection (@bP) is proposed to alleviate the problem without the abovementioned constraints. @bP grounds its theoretical foundation on Beta distribution for easy computation and has stable performance when experimenting with data derived from the public websites of MovieLens.