An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Evaluating collaborative filtering recommender systems
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)
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Attacks and Remedies in Collaborative Recommendation
IEEE Intelligent Systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Robustness of collaborative recommendation based on association rule mining
Proceedings of the 2007 ACM conference on Recommender systems
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Using data mining and recommender systems to scale up the requirements process
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive 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
Effective diverse and obfuscated attacks on model-based recommender systems
Proceedings of the third ACM conference on 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
Impact of relevance measures on the robustness and accuracy of collaborative filtering
EC-Web'07 Proceedings of the 8th international conference on E-commerce and web technologies
On the stability of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
Dependable filtering: Philosophy and realizations
ACM Transactions on Information Systems (TOIS)
A fuzzy recommender system for eElections
EGOVIS'10 Proceedings of the First international conference on Electronic government and the information systems perspective
Analysis of robustness in trust-based recommender systems
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Robustness analysis of model-based collaborative filtering systems
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
Robustness of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Quality and Leniency in Online Collaborative Rating Systems
ACM Transactions on the Web (TWEB)
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Stochastic search for global neighbors selection in collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Stability of Recommendation Algorithms
ACM Transactions on Information Systems (TOIS)
Cluster searching strategies for collaborative recommendation systems
Information Processing and Management: an International Journal
Robust Sybil attack defense with information level in online Recommender Systems
Expert Systems with Applications: An International Journal
Robustness analysis of privacy-preserving model-based recommendation schemes
Expert Systems with Applications: An International Journal
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The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-based collaborative filtering algorithms, such as k- nearest neighbor, have been shown to be quite vulnerable to such attacks. In this paper, we examine the robustness of model-based recommendation algorithms in the face of profile injection attacks. In particular, we consider two recommendation algorithms, one based on k-means clustering and the other based on Probabilistic Latent Semantic Analysis (PLSA). These algorithms aggregate similar users into user segments that are compared to the profile of an active user to generate recommendations. Traditionally, model-based algorithms have been used to alleviate the scalability problems associated with memory-based recommender systems. We show, empirically, that these algorithms also offer significant improvements in stability and robustness over the standard k- nearest neighbor approach when attacked. Furthermore, our results show that, particularly, the PLSA-based approach can achieve comparable recommendation accuracy.