Knowledge-based recommender technologies provide a couple of mechanisms for improving the accessibility of product assortments for customers, e.g., in situations where no solution can be found for a given set of customer requirements, the recommender application calculates a set of repair actions which can guarantee the identification of a solution. Further examples for such mechanisms are explanations or product comparisons. All these mechanisms have a certain effect on the behavior of customers interacting with a recommender application. In this paper we present results from a user study, which focused on the analysis of effects of different recommendation mechanisms on the overall customer acceptance of recommender technologies.