Recommender systems support online consumers in identifying suitable products from vast and complex product assortments. Typically, recommender systems compute choice sets of the top-k ranked products. Based on the actual user choices, the system tunes its prediction function hereafter. However, the existence of decision biases such as position, decoy, or framing effects is disregarded when making recommendations or exploiting implicit user feedback such as choice behavior. In order to purposefully avoid or apply the persuasive traits that are inherent to the presentation of product recommendations, it is crucial to understand the relative strength and interaction between these effects in online environments. This article contributes an experimental analysis of the impact of different decision biases like decoy or position effects as well as risk aversion in positive decision frames on users' choice behavior. The findings document a strong dominance of risk aversion strategies and the need for awareness of these effects.

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