Rationalizing Recommendations (RecomRatio)

In many situations, experts and laymen receive recommendations on how to act, which decisions to take, or which products to purchase. Very often, however, the circumstances and arguments that led to a particular recommendation are not transparent. While in most cases there are various alternatives to choose from, receivers of recommendations are usually under pressure to act, thus lacking time and other resources to systematically compare the recommended option against all available alternatives.

Our goal is to develop a computational method that, given a recommendation, is able to perform a task we call rationalizing recommendations, which consists in setting the recommended option into a more comprehensive context by systematically comparing it to other available options, and generating a comparative argumentative summary describing the relative pros and cons of the recommendation.

To this end, we will develop novel information extraction solutions that employ probabilistic graphical models to extract relevant evidence from scientific literature. We will extend current information retrieval methods to retrieve evidence relevant for generating arguments from heterogeneous document collections. Based on the extracted evidence, our method will generate arguments in favor or against a certain recommendation in comparison to other options with respect to a particular dimension or aspect. To this end, we propose a new data structure called Hierarchical Argumentation Tree that provides the backbone of a hierarchically structured argument. Taking preferences between different comparative dimensions into account, the tree can be used to infer under which  assumptions the given recommendation can be embedded into a valid argument. We will further develop a template-based natural language generation approach which, based on this hierarchical argumentation tree and the preferences made explicit therein, generates a complex yet concise argument in natural language that makes explicit and transparent under which conditions and assumptions the recommended option is the best one compared to other options, thus supporting decision making.

As proof of concept, we apply our method to the case of therapy recommendations in the context of evidence-based medicine. In fact, medical decision making aiming at the optimal therapy for a patient is often based on recommendations accompanying a laboratory diagnosis. By rendering the assumptions and premises underlying such a recommendation explicit and traceable, our method could support physicians in making more informed decisions that are backed by the level-of-evidence of a therapy as described in the current scientific literature. We intend to develop our system in close cooperation with medical experts who will support us in requirement and use case definition, data annotation as well as the final evaluation of the system.