An Approach to Personalized Decision Support Based on Gradual Bipolar Argumentation Enhanced With User Preferences

In order to provide personalized recommendations, decision support methods need to take into account user preferences in the production of their outcomes. This is a particularly relevant issue in the case of argumentative approaches to decision support, which have often focused on the construction of user-agnostic arguments concerning the various options at stake. In particular, while gradual bipolar argumentation (GBA) has been successfully adopted as a formal basis for the realization of decision support in a variety of application domains, none of these previous works involved personalization aspects and, hence, the study of techniques to integrate user-provided preferences into GBA turns out to be an open research question. In this paper, we provide an initial contribution to this investigative direction from both a theoretical and a practical perspective. On the theoretical side, we introduce a property of local coherence to characterize the expected effects of user preferences on argument strength assessment in GBA and provide results concerning the relations between local coherence and global behaviors. On the practical side, we illustrate a preliminary experimentation in the context of a GBA-based review aggregation system extended with the handling of user preferences, which allows us to draw some considerations on the opportunities and challenges of putting the proposed approach into practice.