Utility-based neighbourhood formation for efficient and robust collaborative filtering
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Finding group shilling in recommendation system
WWW '05 Special interest tracks and posters of the 14th 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
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)
Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the third ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Strength of social influence in trust networks in product review sites
Proceedings of the fourth ACM international conference on Web search and data mining
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
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Collaborative filtering is a vitally central technology of personalized recommendation, yet its recommended result is so sensitive to users' preferences that the recommender system has significant vulnerabilities. To overcome the addressed issue, this paper proposes a hybrid decision approach to effectively and efficiently detect profile injection attacks in collaborative recommender systems. Through modifying the algorithms of RDMA (Rating Deviation from Mean Agreement) and WDMA (Weighted Deviation form Mean Agreement), the hybrid decision approach is integrated from these modified algorithms and the UnRAP (Unsupervised Retrieval of Attack Profiles) algorithm. The extensive experiments based on three common attack models show that the proposed detection algorithm is the best comparing with the modified RDMA and WDMA or origin ones, by which the detecting accuracy significantly increases almost 35%, 25%, and 8% than the RMDA, WMDA, and UnRAP algorithms, respectively. Furthermore, for the mixed attack model, we compare it with the UnRAP algorithm and improve the 10% accuracy.