Constraint-based configuration is a successful industrial application of symbolic Artificial Intelligence. It involves selecting a set of components, features, or services that satisfy a given set of user requirements. These requirements, specified by an individual user or a group, guide the configuration system in identifying a solution that aligns with both, user requirements and the constraints defined in the configuration knowledge base. As configuration tasks grow in size and complexity, there is a growing need to integrate machine learning (ML) for increasing algorithmic efficiency and quality of user interaction. This survey provides a comprehensive overview of approaches that combine ML with constraint-based configuration techniques. We highlight key developments including new developments related to the integration of Large Language Models (LLMs) and identify open research challenges.

Group discussions are common in both private and professional meetings. In such discussions, participants often aim to prioritize options, such as activities or features, based on the group's collective preferences. However, aligning individual preferences within a group can be challenging and may result in dissatisfaction. Group recommender systems address this by aggregating diverse and potentially conflicting preferences. This paper investigates the use of large language models (LLMs) for prioritization in meeting-based group recommendations. We show that LLMs can extract individual preferences from meeting transcripts and generate group-level recommendations without requiring additional manual input. Our user study confirms the feasibility and effectiveness of this approach, highlighting the potential of LLMs to enhance group recommendation workflows within AI-driven decision support systems.

Recommender systems help users make better decisions by suggesting products that match their preferences.However, users often do not understand why certain products are recommended, which can reduce trust and satisfaction.While explanations address this issue, they often fail to communicate the individual impact the decision for an item will have.To address this, we present an LLM-based framework for generating consequence-based explanations.These explanations provide comprehensible personalized insights into the positive and negative consequences of user decisions.To support the assessment and selection of the most effective prompting strategy, we introduce evaluation metrics tailored to consequenceaware explanations and systematically compare different prompting strategies for an apartment recommendation example.

Successful software projects depend on the quality of software requirements. Creating high-quality requirements is a crucial step toward successful software development. Effective support in this area can significantly reduce development costs and enhance the software quality. In this paper, we introduce and assess the capabilities of a Large Language Model (LLM) to evaluate the quality characteristics of software requirements according to the ISO 29148 standard. We aim to further improve the support of stakeholders engaged in requirements engineering (RE). We show how an LLM can assess requirements, explain its decision-making process, and examine its capacity to propose improved versions of requirements. We conduct a study with software engineers to validate our approach. Our findings emphasize the potential of LLMs for improving the quality of software requirements.

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.

Persuasive XAI, also known as Persuasive Explainable Artificial Intelligence, refers to the combination of two concepts: explainable artificial intelligence (XAI) and persuasive technology. Persuasive XAI can be applied in decision-support systems in the healthcare domain in various ways to enhance the perception of patients about the outcomes, encourage adherence to treatment plans, and improve overall patient or clinician engagement. In this paper, we aim to analyze state-of-the-art persuasion strategies used in XAI and discuss how to apply them in Clinical Decision Support Systems (CDSS) to support clinicians and patients in making healthcare-related decisions.

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.