Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Fuzzy Reasonings and Its Applications
Fuzzy Reasonings and Its Applications
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A behavior model for persuasive design
Proceedings of the 4th International Conference on Persuasive Technology
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Fuzzy Modeling and Control
Integration of collective knowledge in fuzzy models supporting web design process
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Analysis of multiplayer platform users activity based on the virtual and real time dimension
SocInfo'11 Proceedings of the Third international conference on Social informatics
Eye-Tracking study of user behavior in recommender interfaces
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
The Computer Journal
Multidimensional Social Network in the Social Recommender System
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Recommending interfaces are usually integrated with marketing processes and are targeted to increasing sales with the use of persuasion and influence methods to motivate users to follow recommendations. In this paper is presented an approach based on decomposition of recommending interface into elements with adjustable influence levels. A fuzzy inference model is proposed to represent the system characteristics with the ability to adjust the parameters of the interface to acquire results and increase customer satisfaction.