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.

Abstract User preferences are a crucial input needed by recommender systems to determine relevant items. In single-shot recommendation scenarios such as content-based filtering and collaborative filtering, user preferences are represented, for example, askeywords,categories, anditem ratings. In conversational recommendation approaches such as constraint-based and critiquing-based recommendation, user preferences are often represented on the semantic level in terms ofitem attribute valuesandcritiques. In this article, we provide an overview of preference representations used in different types of recommender systems. In this context, we take into account the fact thatpreferences aren’t stablebut are ratherconstructedwithin the scope of a recommendation process. In which way preferences are determined and adapted is influenced by various factors such aspersonality traits,emotional states, andcognitive biases. We summarize preference construction related research and also discuss aspects of counteracting cognitive biases.

The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.

Requirements Engineering is one of the most important phases in a software project. The elicitation of requirements and the identification of dependencies between these requirements appears to be a challenging task. In this paper, we present an approach to automatically identify requirement dependencies of type requires by using supervised classification techniques. Our results indicate that the implemented approach can detect potential requires dependencies between requirements (formulated on a textual level). We evaluated our approach on a test dataset and figured out that it is possible to identify requirement dependencies with a high prediction quality. We trained and tested our system with different classifiers such as Naive Bayes, Linear SVM, k-Nearest Neighbors, and Random Forest. The results show that Random Forest classifiers correctly predict dependencies with a F1score of ~82%.

Abstract Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

Recently, food recommender systems have received increasing attention due to their relevance for healthy living. Most existing studies on the food domain focus on recommendations that suggest proper food items for individual users on the basis of considering their preferences or health problems. These systems also provide functionalities to keep track of nutritional consumption as well as to persuade users to change their eating behavior in positive ways. Also, group recommendation functionalities are very useful in the food domain, especially when a group of users wants to have a dinner together at home or have a birthday party in a restaurant. Such scenarios create many challenges for food recommender systems since the preferences of all group members have to be taken into account in an adequate fashion. In this paper, we present an overview of recommendation techniques for individuals and groups in the healthy food domain. In addition, we analyze the existing state-of-the-art in food recommender systems and discuss research challenges related to the development of future food recommendation technologies.