Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Persuasive Technology: Using Computers to Change What We Think and Do
Persuasive Technology: Using Computers to Change What We Think and Do
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Explanation in Recommender Systems
Artificial Intelligence Review
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Trust-inspiring explanation interfaces for recommender systems
Knowledge-Based Systems
Representative explanations for over-constrained problems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Generating and evaluating evaluative arguments
Artificial Intelligence
Modelling a receiver's position to persuasive arguments
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Persuasive recommendation: serial position effects in knowledge-based recommender systems
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Consumer Decision Making in Knowledge-Based Recommendation
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
Consumer decision making in knowledge-based recommendation
Journal of Intelligent Information Systems
Designing an explanation interface for proactive recommendations in automotive scenarios
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
ACM Transactions on Interactive Intelligent Systems (TiiS)
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Recommendation technologies support users in the identification of interesting products and services. Beside the wide-spread approaches of collaborative and content-based filtering, knowledge-based recommender technologies gain an increasing importance due to their capability of deriving recommendations for complex products such as financial services, technical equipment, or consumer goods. The identification of best-fitting products is in many cases a complex decision making task which forces users to fall back to different types of decision heuristics. This phenomenon is explained by the theory of bounded rationality of users which is due to their limited knowledge and computational capacity. Specifically in the context of recommender applications bounded rationality acts as a door opener for different types of persuasive concepts which can influence a user's attitudes (e.g., in terms of product preferences) and behavior (e.g., in terms of buying behavior). The major goal of this paper is to provide an overview of such persuasive aspects and possible formalizations in knowledge-based recommender systems.