Argumentation-Based Explanations in Recommender Systems: Conceptual Framework and Empirical Results

Explaining automatically generated recommendations has shown to be an effective means for supporting the user's decision-making process and increasing system transparency. However, present methods mostly provide non-personalized explanations that are presented in an unstructured manner. We propose a framework based on Toulmin's model designed to generate explanations in an argumentative style by presenting supportive as well as critical information about recommended items and their features. Existing research suggests that argumentative explanations cannot be assumed as equally effective for everyone. People rather tend to either apply rational or intuitive decision-making styles that determine which kinds of information are preferably taken into account. In an experimental user study, we investigated the effectiveness of argumentative explanations while considering the moderating effect of these two different cognitive styles. The results indicate that argumentative explanations, as compared to baseline methods, lead to, among others, increased perceived explanation quality, information sufficiency and overall satisfaction with the system. However, this seems only to be true for intuitive thinkers who rely more on explanations in complex decision situations as compared to rational thinkers.