Advanced Methods for Inconsistent Knowledge Management (Advanced Information and Knowledge Processing)
Evaluation of recommender systems: A new approach
Expert Systems with Applications: An International Journal
INCONSISTENCY OF KNOWLEDGE AND COLLECTIVE INTELLIGENCE
Cybernetics and 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
Rate it again: increasing recommendation accuracy by user re-rating
Proceedings of the third ACM conference on Recommender systems
Reusing ontology mappings for query routing in semantic peer-to-peer environment
Information Sciences: an International Journal
Boosting social collaborations based on contextual synchronization: An empirical study
Expert Systems with Applications: An International Journal
Measuring Expertise in Online Communities
IEEE Intelligent Systems
Expert Systems with Applications: An International Journal
Detecting Vicious Users in Recommendation Systems
DESE '11 Proceedings of the 2011 Developments in E-systems Engineering
Preference-based user rating correction process for interactive recommendation systems
Multimedia Tools and Applications
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To improve the performance of the recommendation process, most of recommendation systems (RecSys) should collect better ratings from users. Particularly, rating process is an important task in interactive RecSys which can ask users to correct their own ratings. However, in real world, there are many inconsistencies (e.g., mistakes and missing values) or incorrect in the user ratings. Thereby, expert-based recommendation framework has been studied to select the most relevant experts in a certain item attribute (or value). This kind of RecSys can i) discover user preference and ii) determine a set of experts based on attribute and value of items. In this paper, we propose a consensual recommendation framework integrating multiple experts to conduct correction process. Since the ratings from experts are assumed to be reliable and correct, we first analyze user profile to determine the preference and find out a set of experts. Next, we measure a minimal inconsistency interval (MinIncInt) that might contain incorrect ratings. Finally, we propose solutions to correct the incorrect rating based on ratings from multiple experts.