GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Consistency-based diagnosis of configuration knowledge bases
Artificial Intelligence
A comparison of two compound critiquing systems
Proceedings of the 12th international conference on Intelligent user interfaces
The evaluation of a hybrid critiquing system with preference-based recommendations organization
Proceedings of the 2007 ACM conference on Recommender systems
Constraint-based recommender systems: technologies and research issues
Proceedings of the 10th international conference on Electronic commerce
Persuasion in Knowledge-Based Recommendation
PERSUASIVE '08 Proceedings of the 3rd international conference on Persuasive Technology
Representative explanations for over-constrained problems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Plausible repairs for inconsistent requirements
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Persuasive recommendation: serial position effects in knowledge-based recommender systems
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
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In contrast to customers of bricks and mortar stores, users of online selling environments are not supported by human sales experts. In such situations recommender applications help to identify the products and/or services that fit the user's wishes and needs. In order to successfully apply recommendation technologies we have to develop an in-depth understanding of decision strategies of users. These decision strategies are explained in different models of human decision making. In this paper we provide an overview of selected models and discuss their importance for recommender system development. Furthermore, we provide an outlook on future research issues.