Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Analysis of ratings on trust inference in open environments
Performance Evaluation
Who predicts better?: results from an online study comparing humans and an online recommender system
Proceedings of the 2008 ACM conference on Recommender systems
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
Content-based recommendation systems
The adaptive web
Boosting social collaborations based on contextual synchronization: An empirical study
Expert Systems with Applications: An International Journal
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Evolutionary approach for semantic-based query sampling in large-scale information sources
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
An economic model of user rating in an online recommender system
UM'05 Proceedings of the 10th international conference on User Modeling
Expert Systems with Applications: 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
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In most of the recommendation systems, user rating is an important user activity that reflects their opinions. Once the users return their ratings about items the systems have suggested, the user ratings can be used to adjust the recommendation process.However, while rating the items users can make some mistakes (e.g., natural noises). As the recommendation systems receive more incorrect ratings, the performance of such systems may decrease. In this paper, we focus on an interactive recommendation system which can help users to correct their own ratings. Thereby, we propose a method to determine whether the ratings from users are consistent to their own preferences (represented as a set of dominant attribute values) or not and eventually to correct these ratings to improve recommendation. The proposed interactive recommendation system has been particularly applied to two user rating datasets (e.g., MovieLens and Netflix) and it has shown better recommendation performance (i.e., lower error ratings).