GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
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
Proceedings of the 16th international conference on World Wide Web
Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Communications of the ACM
Social networks and context-aware spam
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Rater Credibility Assessment in Web Services Interactions
World Wide Web
Detecting spammers and content promoters in online video social networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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
Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Uncovering social spammers: social honeypots + machine learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Detecting product review spammers using rating behaviors
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 20th international conference companion on World wide web
Robustness of recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Cross-Domain Collaborative Filtering: A Brief Survey
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
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Social recommender systems largely rely on user-contributed data to infer users' preference. While this feature has enabled many interesting applications in social networking services, it also introduces unreliability to recommenders as users are allowed to insert data freely. Although detecting malicious attacks from social spammers has been studied for years, little work was done for detecting Noisy but Non-Malicious Users (NNMUs), which refers to those genuine users who may provide some untruthful data due to their imperfect behaviors. Unlike colluded malicious attacks that can be detected by finding similarly-behaved user profiles, NNMUs are more difficult to identify since their profiles are neither similar nor correlated from one another. In this article, we study how to detect NNMUs in social recommender systems. Based on the assumption that the ratings provided by a same user on closely correlated items should have similar scores, we propose an effective method for NNMU detection by capturing and accumulating user's "self-contradictions", i.e., the cases that a user provides very different rating scores on closely correlated items. We show that self-contradiction capturing can be formulated as a constrained quadratic optimization problem w.r.t. a set of slack variables, which can be further used to quantify the underlying noise in each test user profile. We adopt three real-world data sets to empirically test the proposed method. The experimental results show that our method (i) is effective in real-world NNMU detection scenarios, (ii) can significantly outperform other noisy-user detection methods, and (iii) can improve recommendation performance for other users after removing detected NNMUs from the recommender system.