Management Science
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Machine Learning - Special issue on multistrategy learning
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
On the enhancement of collaborative filtering by demographic data
Web Intelligence and Agent Systems
Web Intelligence and Agent Systems
An Integrated Environment for the Development of Knowledge-Based Recommender Applications
International Journal of Electronic Commerce
Impacts of decoy elements on result set evaluations in knowledge-based recommendation
International Journal of Advanced Intelligence Paradigms
Calculating Decoy Items in Utility-Based Recommendation
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Asymmetric Dominance- and Compromise Effects in the Financial Services Domain
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Multivariate preference models and decision making with the MAUT machine
UM'03 Proceedings of the 9th international conference on User modeling
| Hi-index | 0.00 |
Recommender systems are common web applications which support users in finding suitable products in large and/or complex product domains. Although state-of-the-art systems manage to accomplish the task of finding and presenting suitable products they show big deficits in their models of human behavior. Time limitations, cognitive capacities and willingness to cognitive effort bound rational decision making which can lead to unforeseen side effects and consequently to sub-optimal decisions. Decoy effects are cognitive phenomena which are omni-present on result pages but state-of-the-art recommender systems are completely unaware of such effects. Due to the fact that such effects constitute one source of irrational decisions their identification and, if necessary, the neutralization of their biasing potential is extremely important. This paper introduces an approach for identifying and minimizing decoy effects on recommender result pages. To support the suggested approach we present the results of a corresponding user study which clearly proves the concept. Moreover, this paper also investigates whether the decreasing impact of decoys on uncertainty levels during decision making is affected by the decoy minimization approach.