Abstract The ability to efficiently resolve conflicts in interactive constraint-based applications is critical for user experience and system reliability. Conflict resolution can be regarded as a specific type of explanation, often denoted as diagnosis. Existing work on integrating machine learning with diagnostic reasoning emphasizes on the combination of hitting set approaches with probabilistic reasoning and memory-based machine learning. An alternative to such two-phase diagnosis approaches is direct diagnosis, which focuses on determining diagnoses without predetermining conflicts. In this article, we utilize diagnosis knowledge from the past to improve diagnosis efficiency while also maintaining user-defined preference criteria. Our approach integrates model-based collaborative filtering (feed-forward neural networks) and other machine learning approaches (e.g., logistic regression and random forest) with direct model-based diagnosis ( FastDiag ). The re-ordering of constraints as input to the diagnosis algorithm increases the efficiency of diagnostic reasoning for determining preference-preserving diagnoses. Through experiments on real-world configuration knowledge bases ( B2C , BusyBox , EA and Linux kernel ), we demonstrate significant runtime improvements and high accuracy in diagnosis prediction. With this, we also contribute to the growing body of literature on combining machine learning and constraint-based reasoning.

Recommender systems play an important role in supporting the achievement of the United Nations sustainable development goals (SDGs). In recommender systems, explanations can support different goals, such as increasing a user’s trust in a recommendation, persuading a user to purchase specific items, or increasing the understanding of the reasons behind a recommendation. In this paper, we discuss the concept of "sustainability-aware persuasive explanations" which we regard as a major concept to support the achievement of the mentioned SDGs. Such explanations are orthogonal to most existing explanation approaches since they focus on a "less is more" principle, which per se is not included in existing e-commerce platforms. Based on a user study in three item domains, we analyze the potential impacts of sustainability-aware persuasive explanations. The study results are promising regarding user acceptance and the potential impacts of such explanations.

Abstract Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sports. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss future research directions.

Constraint-based recommender systems support users in the identification of complex items such as financial services and digital cameras (digicams). Such recommender systems enable users to find an appropriate item within the scope of a conversational process. In this context, relevant items are determined by matching user preferences with a corresponding product (item) assortment on the basis of a pre-defined set of constraints. The development and maintenance of constraint-based recommenders is often an error-prone activity – specifically with regard to the scoping of the offered item assortment. In this paper, we propose a set of offline analysis operations (metrics) that provide insights to assess the quality of a constraint-based recommender system before the system is deployed for productive use. The operations include a.o. automated analysis of feature restrictiveness and item (product) accessibility. We analyze usage scenarios of the proposed analysis operations on the basis of a simplified example digicam recommender.

Abstract Constraint solving is applied in different application contexts. Examples thereof are the configuration of complex products and services, the determination of production schedules, and the determination of recommendations in online sales scenarios. Constraint solvers apply, for example, search heuristics to assure adequate runtime performance and prediction quality. Several approaches have already been developed showing that machine learning (ML) can be used to optimize search processes in constraint solving. In this article, we provide an overview of the state of the art in applying ML approaches to constraint solving problems including constraint satisfaction, SAT solving, answer set programming (ASP) and applications thereof such as configuration, constraint-based recommendation, and model-based diagnosis. We compare and discuss the advantages and disadvantages of these approaches and point out relevant directions for future work.

In this paper, we provide insights into users’ needs regarding the inclusion of explanations in a movie recommender system. We have developed different variants of a movie recommender system prototype corresponding to different types of explanations and conducted an online user study to evaluate related explanations. The experimental results show that users do not always appreciate explanations. They want to see explanations when they are not satisfied with the recommended items. They expect to see explanations showing how well the recommended item meets their preferences. Moreover, explanation goals are interdependent and affect the overall satisfaction of users with the recommender system.