The test-retest reliability of user involvement instruments
Information and Management
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
An economic model of user rating in an online recommender system
UM'05 Proceedings of the 10th international conference on User Modeling
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
Comparison of implicit and explicit feedback from an online music recommendation service
Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Towards computational models of the visual aesthetic appeal of consumer videos
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Comparisons Instead of Ratings: Towards More Stable Preferences
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Preference-based user rate correction process for interactive recommendation systems
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
The design space of opinion measurement interfaces: exploring recall support for rating and ranking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Estimating the magic barrier of recommender systems: a user study
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Users and noise: the magic barrier of recommender systems
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
Proceedings of the sixth ACM conference on Recommender systems
Information Processing and Management: an International Journal
Integrating multiple experts for correction process in interactive recommendation systems
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
User interface adaptation based on user feedback and machine learning
Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion
Preference-based user rating correction process for interactive recommendation systems
Multimedia Tools and Applications
Mining large streams of user data for personalized recommendations
ACM SIGKDD Explorations Newsletter
Big & personal: data and models behind netflix recommendations
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Generation of web recommendations using implicit user feedback and normalised mutual information
International Journal of Knowledge and Web Intelligence
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
User Modeling and User-Adapted Interaction
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Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth of the user's taste. However, users are inconsistent in giving their feedback, thus introducing an unknown amount of noise that challenges the validity of this assumption. In this paper, we tackle the problem of analyzing and characterizing the noise in user feedback through ratings of movies. We present a user study aimed at quantifying the noise in user ratings that is due to inconsistencies. We measure RMSE values that range from 0.557 to 0.8156. We also analyze how factors such as item sorting and time of rating affect this noise.