Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Consumer decision making in knowledge-based recommendation
Journal of Intelligent Information Systems
Status quo bias in configuration systems
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Minimization of decoy effects in recommender result sets
Web Intelligence and Agent Systems
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Recommender systems support internet users in the often awkward task of finding suitable products in a vast and/or complex product assortment. Many different types of recommenders have been developed during the last decade. From a technical point of view those approaches already work well. What has been widely neglected are decision theoretical phenomenons which can severely impact on the optimally of the taken decision as well as on the challenge to take a decision at all. This paper deals with decoy effects, which have already shown big persuasive potential in marketing and related fields. The big question to be answered in this paper is how to automatically calculate decoy effects in order to identify unforeseen side effects. This includes the presentation of a new decoy model, its combination with utility values calculated by a recommender system, an empirical evaluation of the model, and a corresponding user interface, which serves as starting point for controlling and implementing decoy effects in recommender systems.