A first approach to argument-based recommender systems based on defeasible logic programming

Recommender systems have evolved in the last years as specialized tools to assist users in a plethora of computermediated tasks by providing guidelines or hints. Most recommender systems are aimed at facilitating access to relevant items, a situation particularly common when performing web-based tasks. At the same time, defeasible argumentation has evolved as a successful approach in AI to model commonsense qualitative reasoning, with applications in many areas, such as agent theory, knowledge engineering and legal reasoning. This paper presents a first approach towards the integration of web-based recommender systems with a defeasible argumentation framework. The final goal is to enhance practical reasoning capabilities of current recommender system technology by incorporating argument-based qualitative inference.