Broad application of answer set programming (ASP) for declarative problem solving requires the development of tools supporting the coding process. Program debugging is one of the crucial activities within this process. Modern ASP debugging approaches allow efficient computation of possible explanations of a fault. However, even for a small program a debugger might return a large number of possible explanations and selection of the correct one must be done manually. In this paper we present an interactive query-based ASP debugging method which extends previous approaches and finds the preferred explanation by means of observations. The system automatically generates a sequence of queries to a programmer asking whether a set of ground atoms must be true in all (cautiously) or some (bravely) answer sets of the program. Since some queries can be more informative than the others, we discuss query selection strategies which - given user's preferences for an explanation - can find the most informative query reducing the overall number of queries required for the identification of a preferred explanation.

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified.

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified. Finally, the authors present their view on open research issues and opportunities for future work on this topic.