Minimization of decoy effects in recommender result sets

Abstract

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.