Abstract. This paper is based on the observation that the nearest neighbour in a case-based prediction system may not be the best case to explain a prediction. This observation is based on the notion of a decision surface (i.e. class boundary) and the idea that cases located between the target case and the decision surface are more convincing as support for explanation. This motivates the idea of explanation utility, a metric that may be different to the similarity metric used for nearest neighbour retrieval. In this paper we present an explanation utility framework and present detailed examples of how it is used in two medical decision-support tasks. These examples show how this notion of explanation utility sometimes select cases other than the nearest neighbour for use in explanation and how these cases are more convincing as explanations. 1

Abstract. As Case-Based Reasoning has matured as a discipline; the need for a standard means of representing case-based knowledge has come to the fore. While proposals exist for representing the vocabulary and the case-base knowledge containers, there are still no proposed standards for representing similarity or adaptation knowledge. In this paper we present extensions for representing similarity knowledge to CBML, an XML-based CBR language. 1

Abstract. The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendations produced by the systems is apparent. We describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with explanation cases and with some text that highlights aspects of the query and explanation cases. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners. 1

Pervasive computing requires the ability to detect user activity in order to provide situation-specific services. Case-based reasoning can be used for activity recognition by using sensor data obtained from the environment. Pervasive computing systems can grow to be very large, containing many users, sensors, objects and situations, thus raising the issue of scalability. This paper presents a case-based reasoning approach to activity recognition in a smart home setting. An analysis is performed on scalability with respect to case storage, and an ontology-based approach is proposed for case base maintenance. We succeeded in reducing the casebase size by a factor of one thousand, while increasing the accuracy in recognising some activities.

Abstract. Conversational recommender systems have recently emerged as useful alternative strategies to their single-shot counterpart, especially given their ability to expose a user’s current preferences. These systems use conversational feedback to hone in on the most suitable item for recommendation by improving the mechanism that finds useful collaborators. We propose a novel architecture for performing recommendation that incorporates information about the individual performance of neighbours during a recommendation session, into the neighbour retrieval mechanism. We present our architecture and a set of preliminary evaluation results that suggest there is some merit to our approach. We examine these results and discuss what they mean for future research. 1