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
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Classification features for attack detection in collaborative recommender systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Iterative Filtering in Reputation Systems
SIAM Journal on Matrix Analysis and Applications
OrdRec: an ordinal model for predicting personalized item rating distributions
Proceedings of the fifth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
TFMAP: optimizing MAP for top-n context-aware recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Online rating systems are now ubiquitous due to the success of recommender systems. In such systems, users are allowed to rate the items (movies, songs, commodities) in a predefined range of values. The ratings collected can be used to infer users' preferences as well as items' intrinsic features, which are then matched to perform personalized recommendation. Most previous work focuses on improving the prediction accuracy or ranking capability. Little attention has been paid to the problem of spammers or low-reputed users in such systems. Spammers contaminate the rating system by assigning unreasonable scores to items, which may affect the accuracy of a recommender system. There are evidences supporting the existence of spammers in online rating systems. Reputation estimation methods can be employed to keep track of users' reputation and detect spammers in such systems. In this paper, we propose a unified framework for computing the reputation score of a user, given only users' ratings on items. We show that previously proposed reputation estimation methods can be captured as special cases of our framework. We propose a new low-rank matrix factorization based reputation estimation method and demonstrate its superior discrimination ability.