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
Getting to know you: learning new user preferences in recommender systems
Proceedings of the 7th international conference on Intelligent user interfaces
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
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Proceedings of the 2007 ACM conference on Recommender systems
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
Characterisation of explicit feedback in an online music recommendation service
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
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
Integrating user feedback with heuristic security and privacy management systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Temporal defenses for robust recommendations
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Multi-value probabilistic matrix factorization for IP-TV recommendations
Proceedings of the fifth ACM conference on Recommender systems
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
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted 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
Like-Minded communities: bringing the familiarity and similarity together
WISE'12 Proceedings of the 13th international conference on Web Information Systems Engineering
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
Semi-automatic generation of recommendation processes and their GUIs
Proceedings of the 2013 international conference on Intelligent user interfaces
Preference-based user rating correction process for interactive recommendation systems
Multimedia Tools and Applications
Rating Bias and Preference Acquisition
ACM Transactions on Interactive Intelligent Systems (TiiS)
Interactive collaborative filtering
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Rating support interfaces to improve user experience and recommender accuracy
Proceedings of the 7th ACM conference on Recommender systems
Generation of web recommendations using implicit user feedback and normalised mutual information
International Journal of Knowledge and Web Intelligence
User Modeling and User-Adapted Interaction
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A common approach to designing Recommender Systems (RS) consists of asking users to explicitly rate items in order to collect feedback about their preferences. However, users have been shown to be inconsistent and to introduce a non-negligible amount of natural noise in their ratings that affects the accuracy of the predictions. In this paper, we present a novel approach to improve RS accuracy by reducing the natural noise in the input data via a preprocessing step. In order to quantitatively understand the impact of natural noise, we first analyze the response of common recommendation algorithms to this noise. Next, we propose a novel algorithm to denoise existing datasets by means of re-rating: i.e. by asking users to rate previously rated items again. This denoising step yields very significant accuracy improvements. However, re-rating all items in the original dataset is unpractical. Therefore, we study the accuracy gains obtained when re-rating only some of the ratings.In particular, we propose two partial denoising strategies: data and user-dependent denoising. Finally, we compare the value of adding a rating of an unseen item vs. re-rating an item. We conclude with a proposal for RS to improve the quality of their user data and hence their accuracy: asking users to re-rate items might, in some circumstances, be more beneficial than asking users to rate unseen items.