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.

The primary goal of Recommender Systems is to suggest the most suitable items to a user, aligning them with the user’s interests and needs. RSs are essential for modern e-commerce, helping users discover content and products by predicting suitable items based on their past behavior. However, their success isn’t just about advanced algorithms. The design of the user interface and a good integration with the human decision-making process are equally crucial. A well-designed interface enhances the user experience and makes recommendations more effective, while a poor interface can lead to frustration. Recognizing this limitation, recent trends in Recommender Systems (RSs) are increasingly focusing on integrating Symbiotic Human-Machine Decision-Making models. These models aim to offer users a dynamic and persuasive interface that helps them better understand and engage with recommendations. This shift is a crucial step toward developing recommender systems that truly connect with users and offer a more enjoyable, trustworthy, explainable, and user-friendly experience. Although early efforts concentrated on creating systems that could proactively predict user preferences and needs, modern RSs also emphasize the importance of providing users with control and transparency over their recommendations. Finding the right balance between proactivity and user control is essential to ensure that the system supports users without being too intrusive, thus improving their overall satisfaction. As Large Language Models (LLMs) become more integrated into recommender systems, the importance of user-centric interfaces and a deep understanding of decision-making becomes even more critical. Effective integration of LLMs requires interfaces that are both visually and cognitively engaging. These aspects are the main discussion topics of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’24. In this summary, we introduce the motivation and perspective of the workshop, review its history, and discuss the most critical issues that deserve attention for future research directions.

Users are often confronted with situations where they have to decide in favor or against an offered item, like a book, movie, or recipe. Those suggested items are commonly determined by a recommender system, which considers personal preferences to identify relevant items. However, those systems often lack transparency and comprehensibility in revealing why a specific item is recommended. For this purpose, explanations have been added as a powerful tool to help users with their final decisions. In this paper, we present and evaluate the capabilities of a Large Language Model (LLM) to come up with high-quality explanations to further improve the support of users for three different recommendation approaches, including feature-based recommendation, collaborative filtering, and knowledge-based recommendation. We explain how an LLM can be applied to generate personalized explanations and evaluate the explanation goals in an online user study. Our findings highlight that LLM-generated explanations are highly appreciated by users as they help in the evaluation of recommended items. Furthermore, we discuss which characteristics of the LLM-based explanations were perceived positively and how those findings can be used for future research.

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.

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.

The methods and techniques introduced in the previous chapters provide a basic means to aggregate the preferences of individual group members and to determine recommendations suitable for the whole group. However, preference aggregation can go beyond the integration of the preferences of individual group members. In this chapter, we show how to take into account the aspects of personality, emotions, and group dynamics when determining item predictions for groups. We summarize research related to the integration of these aspects into recommender systems and provide some selected examples.

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 .

The 10th edition of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems was held as part of the 17th ACM Conference on Recommender Systems (RecSys), the premier international forum for the presentation of new research results, systems and techniques in the broad field of recommender systems. The workshop was organized as a hybrid event: the physical session took place on September 18th at the venue of the main conference, Singapore, with the possibility for authors to present remotely. The IntRS workshop brings together an interdisciplinary community of researchers and practitioners who share research on new recommender systems (informed by psychology), including new design technologies and evaluation methodologies, and aim to identify critical challenges and emerging topics in the field. This year we focused particularly on topics related to Human-Centered AI, Explainability of decision-making models, User-adaptive XAI systems, which are becoming more and more popular in the last years, especially in domains where recommended options might have ethical and legal impacts on users. The integration of XAI with recommender systems is crucial for enhancing their transparency, interpretability, and accountability. This topic attracted a lot of interest from the community, as demonstrated by the fact that several workshop papers proposed methods for recommendation explanations. Date : 18 September 2023. Website : https://intrs2023.wordpress.com.

Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.

The concepts and semantics of constraint solving and configuration need to be understood in order to be able to develop one’s own configuration knowledge bases. Developing a related basic understanding is in many cases quite challenging. Consequently, further support is needed that makes the learning of configuration knowledge representation practices and semantics less effortful. In this paper, we provide a short overview of ConGuess which is a game-based learning environment for constraint-based configuration tasks. In this context, we report the results of a user study which focused on an analysis of the perceived complexity of different constraint types and on a corresponding usability analysis.

The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

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 .

The development and maintenance of feature models is often an error-prone activity requiring different types of analysis operations that help developers to restore required feature model properties. Fulfilling such properties helps to assure compliance between feature model and corresponding domain variability properties and -at the same time - helps to increase feature model maintainability. In this paper, we propose a set of additional analysis operations that provide insights regarding potential impacts of applying feature models in constraint-based recommendation scenarios where feature models are used to define user preference spaces. Our proposed analysis operations provide a.o. insights into aspects such as feature restrictiveness and product accessibility when applying a constraint-based recommender system. We analyze usage scenarios of the operations on the basis of an example implementation with a digital camera feature model and discuss open research issues.

In many scenarios, configurators support the configuration of a solution that satisfies the preferences of a single user. The concept of \emph{multi-configuration} is based on the idea of configuring a set of configurations. Such a functionality is relevant in scenarios such as the configuration of personalized exams, the configuration of project teams, and the configuration of different trips for individual members of a tourist group (e.g., when visiting a specific city). In this paper, we exemplify the application of multi-configuration for generating individualized exams. We also provide a constraint solver performance analysis which helps to gain some insights into corresponding performance issues.