Minimization of Product Utility Estimation Errors in Recommender Result Set Evaluations
Recommender systems are wide-spread web applications which can effectively support users in finding suitable products in a large and/or complex product domain. Although state-of-the-art systems manage to accomplish the task of finding and presenting suitable products they show big deficits in the applied model of human behavior. Time limitations, cognitive capacities, and willingness to cognitive effort bound rational decision taking which can lead to unforeseen side effects and furthermore to sub-optimal decisions. Decoy effects are cognitive phenomenons which are omni-present on result pages. 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 undergird the presented approach we present the results of a corresponding user study which clearly proofs the concept.