Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Analysis of ratings on trust inference in open environments
Performance Evaluation
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
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
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
An economic model of user rating in an online recommender system
UM'05 Proceedings of the 10th international conference on User Modeling
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In recommendation systems, rating is an important user activity reflecting their opinions. Once the users return their rates about the items suggested from the systems, the user rates can be used to adjust recommendation process. However, users can make some mistakes (e.g., nature noises) during rating the items. As the recommendation systems receive more incorrect rates, the performance of such systems might be decreased. To solve the problem, in this paper, we focus on an interactive recommendation system which can help users to correct their own rates.