GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Developing trust in recommender agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
Recommender systems: attack types and strategies
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Lies and propaganda: detecting spam users in collaborative filtering
Proceedings of the 12th international conference on Intelligent user interfaces
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Robust collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
Attack resistant collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
The information cost of manipulation-resistance in recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Unsupervised retrieval of attack profiles in collaborative recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative web search: a robustness analysis
Artificial Intelligence Review
Manipulation-resistant recommender systems through influence limits
ACM SIGecom Exchanges
Unsupervised strategies for shilling detection and robust collaborative filtering
User Modeling and User-Adapted Interaction
Noise Detection in Agent Reputation Models Using IMM Filtering
Trust in Agent Societies
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
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
Effective diverse and obfuscated attacks on model-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
Coping with noisy search experiences
Knowledge-Based Systems
Characterisation of explicit feedback in an online music recommendation service
Proceedings of the fourth ACM conference on Recommender systems
A user trust-based collaborative filtering recommendation algorithm
ICICS'09 Proceedings of the 11th international conference on Information and Communications Security
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Proceedings of the sixth ACM conference on Recommender systems
Preference-based user rating correction process for interactive recommendation systems
Multimedia Tools and Applications
Rating support interfaces to improve user experience and recommender accuracy
Proceedings of the 7th ACM conference on Recommender systems
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In this paper, we propose a framework that enables the detection of noise in recommender system databases. We consider two classes of noise: natural and malicious noise. The issue of natural noise arises from imperfect user behaviour (e.g. erroneous/careless preference selection) and the various rating collection processes that are employed. Malicious noise concerns the deliberate attempt to bias system output in some particular manner. We argue that both classes of noise are important and can adversely effect recommendation performance. Our objective is to devise techniques that enable system administrators to identify and remove from the recommendation process any such noise that is present in the data. We provide an empirical evaluation of our approach and demonstrate that it is successful with respect to key performance indicators.