In this chapter, our aim is to show how group recommendation can be implemented on the basis of recommendation paradigms for individual users. Specifically, we focus on collaborative filtering, content-based filtering, constraint-based, critiquing-based, and hybrid recommendation. Throughout this chapter, we differentiate between (1) aggregated predictions and (2) aggregated models as basic strategies for aggregating the preferences of individual group members.

Recommender systems are decision support systems helping users to identify one or more items (solutions) that fit their wishes and needs. The most frequent application of recommender systems nowadays is to propose items to individual users. However, there are many scenarios where a group of users should receive a recommendation. For example, think of a group decision regarding the next holiday destination or a group decision regarding a restaurant to visit for a joint dinner. The goal of this book is to provide an introduction to group recommender systems, i.e., recommender systems that determine recommendations for groups. In this chapter, we provide an introduction to basic types of recommendation algorithms for individual users and characterize related decision tasks. This introduction serves as a basis for the introduction of group recommendation algorithms in Chap. 2 .

In this chapter, we present an overview of different group recommender applications. We organize this overview into the application domains of music, movies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chap. 2 .

Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific goals such as increasing the transparency of a recommendation or increasing a user’s trust in the recommender system. In this chapter, we provide an overview of existing research related to explanations in recommender systems and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of aggregated predictions and aggregated models strategies.

Decision biases can be interpreted as tendencies to think and act in specific ways that result in a systematic deviation of potentially rational and high-quality decisions. In this chapter, we provide an overview of example decision biases and show possibilities to counteract these. The overview includes (1) biases that exist in both single user and group decision making (decoy effects, serial position effects, framing, and anchoring) and (2) biases that especially occur in the context of group decision making (GroupThink, polarization, and emotional contagion).

Current crises (such as the war in Ukraine) are confronting end consumers with ever new challenges, for example, in the context of energy management. This paper presents a configurator that enables end-users to make certain functions in their household "smart" and thus, for instance, get a better grip on their energy management. The concept is based on constraint programming, concretely on the open source platform Choco 1 . After emphasizing the algorithmic problems and challenges, a first version of a prototype is presented, which illustrates the possibilities of guiding end-users through the process of making their living environments smart. Related possibilities, potentials and challenges of the proposed approach are discussed at the end of the paper.

Configuration is composing product models of complex and variant systems requiring parameterizable components. This traditionally relies on knowledge-representation formalisms that enable the reasoning and automatization of different software engineering tasks when developing such systems. The main goal of this workshop is to promote research in the application areas related to configuration. Traditionally, this workshop has managed to bridge the gap with contributions from both the academy and the industry, thus, enabling the exchange of ideas and challenges. It provides a forum for discussing ideas, evaluations and experiences, especially in using AI techniques for solving configuration problems.

Recommender systems are systems that help users in decision-making situations where there is an abundance of choices. We can find them in our everyday lives, for example in online shops. State-of-the-art research in recommender systems has shown the benefits of behavioural modeling. Behavioural modeling means that we use past ratings, purchases, clicks etc. to model the user preferences. However, behavioural modeling is not able to capture certain aspects of the user preferences. In this talk I will show how the usage of complementary research in cognitive models, such as personality and emotions, can benefit recommender systems.

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.

. Exam cheating indicates behaviors of students to fraud-ulently achieve their desired grades through various forms, such as item harvesting, item pre-knowledge, item memorizing, collusion and answer copying, and answer checking from available sources. Such dishonesty behaviors become manifest in e-learning scenarios, where exams are often conducted via online assessment platforms without the physical supervision of proctors. In this paper, we pro-pose an approach to counteract exam cheating based on configura-tion and recommendation techniques. Our approach allows examiners to configure questions and exams using feature models. We support the configuration of parameterized questions, which helps to generate a large number of exam instances. Besides, a content-based recommendation mechanism is integrated into the exam configura-tion process, which helps examiners to select questions that have not appeared in the latest exams. We also propose mock-ups to show how question and exam generation processes can be proceeded in a real exam generator system.

Constraint-based recommenders support users in the identification of items (products) fitting their wishes and needs. Example domains are financial services and electronic equipment. In this paper we show how divide-and-conquer based (direct) diagnosis algorithms (no conflict detection is needed) can be exploited in constraint-based recommendation scenarios. In this context, we provide an overview of the MediaWiki-based recommendation environment WeeVis.

Explanations are used in recommender systems for various reasons. Users have to be supported in making (high-quality) decisions more quickly. Developers of recommender systems want to convince users to purchase specific items. Users should better understand how the recommender system works and why a specific item has been recommended. Users should also develop a more in-depth understanding of the item domain. Consequently, explanations are designed in order to achieve specific \emph{goals} such as increasing the transparency of a recommendation or increasing a user's trust in the recommender system. In this paper, we provide an overview of existing research related to explanations in recommender systems, and specifically discuss aspects relevant to group recommendation scenarios. In this context, we present different ways of explaining and visualizing recommendations determined on the basis of preference aggregation strategies.

The elicitation of user preferences represents an effective means to identify the relevant user preference requirements for a configuration task. One common method to determine the preferences of users is to survey users using dialogs that consist of (multiple-choice) questions with selectable answers. A major drawback of dialog-based preference elicitation is that many new users are not willing to answer a fairly large number of questions which is a prerequisite for the identification of user constraints. To that end, we have developed a novel similarity-based approach which aims to solve such ramp-up (cold-start) scenarios by automatically completing a set of remaining questions in a preference elicitation dialog given a small set of pre-answered questions. Our approach has been evaluated with two small real-world datasets. Initial evaluation results reveal that our approach is able to find the most probable answers a respondent is likely to give to a set of remaining questions. First insights of an evaluation also show that our approach can keep the number of initial questions at a very low level, meaning that only between 35% and 50% of questions have to be asked in most cases in order to predict the complete set of user requirements. The results also indicate that this level can be further reduced with increasing amounts of training data.

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work.

Requirements engineering is one of the most critical phases in the context of software development. Unclear textual specifications of requirements, hidden dependencies between requirements, and suboptimal prioritizations and release plans represent the major reasons for project delays and even cancellation. In this paper, we show how group recommender user interfaces can help to improve the quality of requirements engineering processes. To that end, we developed a novel group recommendation approach that focuses on the aspect of improving requirements prioritization by making preference elicitation processes more flexible as well as by introducing innovative user interfaces that foster information exchange among stakeholders. We conducted a large user study (N=313 participants) to evaluate our approach. The evaluation results indicate that argumentation-based user interfaces in a group setting trigger more rating and communication activity among the group members which significantly improves the quality of the prioritization process. Our main contributions are twofold: (1) more flexibility of the requirements evaluation by supporting the delegation of votes to experts and (2) an increased engagement of the stakeholders responsible for the requirements.

Explanations are integrated into recommender systems to give users an insight into the recommendation generation process. 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. In this paper, we proposed different types of explanations and found the most effective ones in terms of increasing 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 consider the preferences of all or the majority of group members achieve the best results in terms of the mentioned dimensions. Besides, we discovered positive correlations among these aspects. In the context of repeated decisions, group members' satisfaction from previous decisions are helpful to improve the fairness perception of users concerning group recommendations and speed up the group decision-making process. Furthermore, we found out that gender diversity does influence the perception of users regarding the mentioned dimensions of the explanations. Although the proposed explanations were analyzed in group decision scenarios for non-configurable (no-attribute) items, there exist potential possibilities to apply them to explanations for configurable items.

Requirements Engineering (RE) represents a critical phase in the management and planning of software projects. One of the main reasons for project failure is missing or incomplete RE. In order to reduce the risk of project failure, there exists a high and urgent demand for applying intelligent technologies in RE. Since the RE process is mainly decision- and community-driven, Recommender Systems are supposed to be applied in this particular context to support stakeholders in decision-making and, hence, to increase the quality of the decisions taken by the stakeholders. This paper introduces a variety of innovative recommendation tools developed within the scope of the European Horizon 2020 research project OPENREQ. Moreover, we give an overview of user studies conducted to evaluate our approaches and present final results of selected studies. The study results indicate that the developed concepts have the potential to significantly improve the quality of requirements definition and requirements prioritization.