Abstract
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.
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Teppan, E.C., Felfernig, A. (2009). Calculating Decoy Items in Utility-Based Recommendation. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_19
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DOI: https://doi.org/10.1007/978-3-642-02568-6_19
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