June 20, 2007 14:11 World Scientific Review Volume - 9in x 6in 00541 Chapter 1 User Acceptance of Knowledge-based Recommenders Alexander Felfernig and Erich Teppan ∗ Intelligent Systems and Business Informatics, University Klagenfurt Universitaetsstrasse 65-67, Klagenfurt, A-9020, Austria Bartosz Gula † Cognitive Psychology, University Klagenfurt Universitaetsstrasse 65-67, Klagenfurt, A-9020, Austria Recommender applications support decision-making processes by help- ing online customers to identify products more effectively. Recommen- dation problems have a long history as a successful application area of Artificial Intelligence (AI) and the interest in recommender applications has dramatically increased due to the demand for personalization tech- nologies by large and successful e-Commerce environments. Knowledge- based recommender applications are especially useful for improving the accessibility of complex products such as financial services or comput- ers. Such products demand a more profound knowledge from customers than simple products such as CDs or movies. In this paper we focus on a discussion of AI technologies needed for the development of knowledge- based recommender applications. In this context, we report experiences from commercial projects and present the results of a study which inves- tigated key factors influencing the acceptance of knowledge-based rec- ommender technologies by end-users. 1.1. Introduction Recommender applications support online customers in the effective iden- tification of products and services suiting their wishes and needs. These applications are of particular importance for increasing the accessibility of product assortments for users not having a detailed product domain knowl- * felfernig@uni-klu.ac.at, teppan@uni-klu.ac.at † gula@uni-klu.ac.at 333