Detecting minimal conflict sets is essential for providing meaningful feedback in knowledge-based configuration. While lazy conflict detection addresses runtime efficiency by predetermining conflict sets offline using a genetic algorithm, it suffers from low conflict coverage, stagnation, and instability. We propose a robust enhancement that integrates multi-conflict extraction and genetic diversity control to overcome these limitations. Our method extends conflict discovery per evaluation and introduces three diversity mechanisms: full population reproduction, weighted genetic operators, and adaptive extinction. Empirical evaluations on five real-world configuration knowledge bases show that our approach recovers up to 85% of conflict sets, reduces solver calls by up to 73%, and achieves higher result stability. These improvements demonstrate the scalability and reliability of enhanced lazy conflict detection for interactive configuration systems.

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.

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.

Conflict detection is relevant in various application scenarios, ranging from interactive decision-making to the diagnosis of faulty knowledge bases. Conflicts can be regarded as sets of constraints that cause an inconsistency. In many scenarios (e.g., constraint-based configuration), conflicts are repeatedly determined for the same or similar sets of constraints. This misses out on the valuable opportunity for leveraging knowledge reuse and related potential performance improvements, which are extremely important, specifically interactive constraint-based applications. In this paper, we show how to integrate knowledge reuse concepts into non-instructive conflict detection. We introduce the InformedQX algorithm, which is a reuse-aware variant of QuickXPlain. The results of a related performance analysis with the Linux-2.6.3.33 configuration knowledge base show significant improvements in terms of runtime performance compared to QuickXPlain.

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.

Abstract Group decision-making processes can be supported by group recommender systems that help groups of users obtain satisfying decision outcomes. These systems integrate a consensus-achieving process, allowing group members to discuss with each other on the potential items, adapt their opinions accordingly, and achieve an agreement on a selected item. Such a process, therefore, helps to generate group recommendations with a high satisfaction level of group members. Our article provides a rigorous review of the existing consensus approaches to group decision-making. These approaches are classified depending on the applied consensus models such asreference domainwhere a set of group members or items is selected for calculating consensus measures,coincidence methodthat calculates the consensus degree between group members depending on the coincidence concept,operatorsthat aggregate user preferences,guidance measureswhere the consensus-achieving process is guided by different consensus measures, andrecommendation generationandindividual centralitythat enhance the role of a moderator or a leader in the consensus-achieving process. Further consensus techniques for group decision-making in heterogeneous and large-scale groups are also discussed in this article. Besides, to provide an overall landscape of consensus approaches, we also discuss new consensus models in group recommender systems. These models attempt to improve basic aggregation strategies, further consider social relationship interactions, and provide group members with intuitive descriptions regarding the current consensus state of the group. Finally, we point out challenges and discuss open topics for future work.

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the “no solution could be found” dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without pre-determining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm’s runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base.

The increasing size and complexity of feature models (FMs) can trigger anomalies or faults, challenging stakeholders in keeping FMs consistent with the domain requirements. Existing quality assurance tools do not provide advanced techniques to point out possibilities to adapt an FM for consistency recovery. In this paper, we present FMTESTING, which is a plug-in for FEATUREIDE, an ECLIPSE-based IDE supporting different phases of feature-oriented software development. FMTESTING is capable of automatically generating property-based test cases based on six different types of FM analysis operations. Furthermore, for violated test cases, diagnoses are provided to precisely indicate faulty FM elements (constraints) that should be adapted to restore consistency. Our tool provides user interfaces inside FEATUREIDE to ensure convenient use, even for users who are not domain experts.

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.

Explanations can be provided with different goals, such as clarifying how the system works, how well the recommended item meets the user’s preferences, and how an explanation helps the user select an item faster. Although extensive research has been conducted in this research line, not much attention is paid to investigating user needs for explanations. To the best of our knowledge, no studies provide related insights, especially from the perspectives of item domain and personal characteristics. Up to now, it is not completely clear if user needs for explanations change across different item domains and vary according to user characteristics. To analyze these aspects, we developed three web-based prototype recommender systems for low-, average-, and high-involvement item domains and conducted a user study with 553 participants from different countries. Related results show that, in high-involvement item domains, users tend to have a look at explanations when they are not satisfied with the recommended items. An opposite tendency was found in low- and average-involvement item domains. Statistically, there is insufficient evidence to suggest correlations between users’ needs for explanations and item domains or between users’ needs and personal characteristics. However, the descriptive statistics show that users’ need for explanations varies across different item domains. In this study, we also found the best explanation approaches to be used in a specific recommendation domain.

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.

Explanations help users to better understand why a set of items has been recommended. Compared to single user recommender systems, explanations in group recommender systems have further goals. Examples thereof are fairness which helps to take into account as much as possible group members' preferences and consensus which persuades group members to agree on a decision. This paper proposes different explanation types and investigates which explanation best helps to increase the fairness perception, consensus perception, and satisfaction of group members with regard to group recommendations. We conducted a user study to evaluate the proposed explanations. The results show that explanations which take into account preferences of all or the majority of group members achieve the best results in terms of the mentioned aspects. Moreover, there exist positive correlations among these aspects, i.e., as the perceived fairness (or the perceived consensus) of explanations increases, so does the satisfaction of users with regard to group recommendations. In addition, in the context of repeated decisions, the inclusion of group members' satisfaction from previous decisions in the explanations helps to improve the fairness perception of users with regard to group recommendations.