Group Recommender Systems (GRSys) are designed to recommend items that address the needs of groups of people. Compared to individual users, groups are dynamic entities where interpersonal relationships, group dynamics, emotional contagion, etc., substantially affect the group’s needs. Nevertheless, these characteristics are often poorly defined or overlooked in system modeling. The fifth GMAP workshop brought together a community of scholars focused on group modeling, adaptation, and personalization. The event was dedicated to exploring the challenges and opportunities of supporting collective decision-making, fostering interdisciplinary dialogue, and forging new collaborations. The three presented papers covered a diverse range of topics, revisiting assumptions about similarity, task definitions, and fairness perception in particular scenarios within the realm of group modeling, adaptation, and personalization.

Adaptive and personalized systems increasingly mediate everyday digital experiences, and recent advances in LLMs, NLP, and Generative AI have amplified their reach, from intelligent user interfaces and conversational agents to AR/immersive interactions and autonomous assistants. These methods now support applications in health and well-being, behavior change and persuasion, e-learning and educational games, and group modeling for collaboration and team formation, all of which require increasingly rich and dynamic user models. At the same time, modern pipelines based on data mining, knowledge graphs/linked data, semantic representations, and affective computing raise urgent questions about transparency, privacy, fairness, accountability, and user understanding, reinforced by regulatory expectations such as the GDPR right to explanation. Yet research often optimizes personalization performance without comparable attention to interpretability and human comprehension. This workshop provides a forum for theoretical, methodological, and empirical work that bridges effectiveness and explainability, with emphasis on robust human-centered evaluation and reproducible practices, including benchmarks, datasets, and shared challenges that advance trustworthy personalization in an era of increasingly autonomous AI.

In everyday life, we make decisions in groups about a variety of issues. In group decision-making, group members discuss options, exchange preferences and opinions, and make a common decision. Decision support systems and group recommender systems facilitate this process by enabling preference elicitation, generating recommendations, and supporting the process. We are here interested in building a conversational system, namely, a chat app, enhanced with an AI agent supporting the group decision-making process. To design the system, rather than solely relying on our assumptions, we took one step back and conducted a comprehensive focus group study. This approach has allowed us to gain original insights into the specific needs and preferences of the future end-users, i.e., group members, ensuring that our system design aligns more closely with their requirements. The focus group study involved fourteen participants in three group compositions: friends, families, and couples. Our findings reveal that most of the group members define a good choice as one that maximizes overall satisfaction without leaving any member dissatisfied. Dealing with competing group members emerged as a primary concern, with study participants requesting specific help from the AI agent to address this challenge. Participants identified personality and group structure as crucial characteristics for the AI agent to properly operate, though some expressed privacy concerns. Lastly, participants expected an AI agent to provide private interactions with individual members, proactively guide discussions when necessary, continually analyze group interactions, and tailor support to those interactions.

Group recommender systems (GRSs) identify items to recommend to a group of people by aggregating group members’ individual preferences into a group profile and selecting the items that have the largest score in the group profile. The GRS predicts that these recommendations would be chosen by the group by assuming that the group is applying the same preference aggregation strategy as the one adopted by the GRS. However, predicting the choice of a group is more complex since the GRS is not aware of the exact preference aggregation strategy that is going to be used by the group. To this end, the aim of this article is to validate the research hypothesis that, by using a machine learning approach and a dataset of observed group choices, it is possible to predict a group’s final choice better than by using a standard preference aggregation strategy. Inspired by the Decision Scheme theory, which first tried to address the group choice prediction problem, we search for a group profile definition that, in conjunction with a machine learning model, can be used to accurately predict a group choice. Moreover, to cope with the data scarcity problem, we propose two data augmentation methods, which add synthetic group profiles to the training data, and we hypothesize that they can further improve the choice prediction accuracy. We validate our research hypotheses by using a dataset containing 282 participants organized in 79 groups. The experiments indicate that the proposed method outperforms baseline aggregation strategies when used for group choice prediction. The method we propose is robust with the presence of missing preference data and achieves a performance superior to what humans can achieve on the group choice prediction task. Finally, the proposed data augmentation method can also improve the prediction accuracy. Our approach can be exploited in novel GRSs to identify the items that the group is likely to choose and to help groups to make even better and fairer choices.

Group Recommender Systems (GRSs), unlike recommendations for individuals, provide suggestions for groups of people. Clearly, many activities are often experienced by a group rather than an individual (visiting a restaurant, traveling, watching a movie, etc.) hence the requirement for such systems. The topic is gradually receiving more and more attention, with an increased number of papers published at significant venues, which is enabled by the predominance of online social platforms that allow their users to interact in groups, as well as to plan group activities. However, the research area lacks certain ground rules, such as basic evaluation agreements. We believe this is one of the main obstacles to make advances in the research area, and to enable researchers to compare and continue each others’ works. In other words, setting the basic evaluation agreements is a stepping-stone towards reproducible Group Recommenders research. The goal of this tutorial is to tackle this problem, by providing the basic principles of the GRSs offline evaluation approaches.

Explanations can help users to better understand why items have been recommended. Additionally, explanations for group recommender systems need to consider further goals than single-user recommender systems. For example, we need to balance group members’ need for privacy with their need for transparency, since a transparent explanation might pose a privacy hazard. In an online experiment with real groups (n=114 participants: 38 groups of size 3), we seek to understand which factors influence people’s privacy concerns when a single explanation is presented to a group in the tourism domain. In particular, we study the direct effects of three factors on privacy concern: a) group members’ personality (using the ‘Big Five’ personality traits), b) specific preference scenarios (i.e., having minority or majority preferences compared to two other group members), c) the type of relationship they have in the group (i.e., loosely coupled heterogeneous, versus tightly coupled homogeneous). We find that for personality two traits, Extroversion, and Agreeableness, each significantly affects the privacy concern. Moreover, having the minority or majority preferences in the group, as well as the type of relationship people have in the group, have a strong and significant influence on participants’ privacy concern. These results suggest that explanations presented to groups need to be adapted to all three factors (personality, type of relationship, and preference scenario) when considering the privacy concern of users.

Over the years, the Web has become a premier source of information in almost every area we can think about. When considering tourism, the Web became the primary source of information for travelers. When planning trips, people search for information about destinations, accommodations, attractions, means of transportation, in short, everything related to their future trip. Once done searching they reserve almost everything online. The blessing of the easily accessible information comes with the curse of information overload. This brings Web search techniques and recommendation systems come into play. This is especially true recently with the appearance of COVID-19 and the uncertainty and transformative power it brings to travelling. WebTour 2021 brings together researchers and practitioners working on developing and improving tools and techniques for improving users ability to better find relevant information that matches their needs.

The workshop features presentations of accepted contributions to the RecSys Challenge 2020, organized by Politecnico di Bari, Free University of Bozen-Bolzano, TU Wien, University of Colorado, Boulder, and Universidade Federal de Campina Grande, and sponsored by Twitter. The challenge focuses on a real-world task of Tweet engagement prediction in a dynamic environment. The goal is to predict the probability for different types of engagement (Like, Reply, Retweet, and Retweet with comment) of a target user for a set of Tweets, based on heterogeneous input data. To this end, Twitter has released a large public dataset of ~160M public Tweets, obtained by subsampling within ~2 weeks, that contains engagement features, user features, and Tweet features. A peculiarity of this challenge is related to the recent regulations on data protection and privacy. The challenge data set was compliant: if a user deleted a Tweet, or their data from Twitter, the dataset was promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics.

Group recommender systems generate recommendations for a group by aggregating individual members’ preferences and finding items that are liked by most of the members. In this paper we introduce a new approach to preference aggregation and group choice prediction that is based on a new form of weighting individuals’ preferences. The approach is based on network science, and, in particular, it relies on the computation of node centrality scores in preferences similarity networks of groups. We also motivate and introduce a non-linear (exponential) remapping of the individuals’ preferences. Based on offline experiments we demonstrate: 1) non-linear remapping of preferences is useful to better predict group choices and generate recommendations; and 2) our weighted approach predicts the actual group choices more accurately than current state-of-the-art methods for group recommendations.

Recommender systems for groups are becoming increasingly popular since many information needs originate from group and social activities, such as listening to music, watching movies, traveling, etc. There has been substantial progress on systems which recommend items to groups of users. However, many challenges remain. The goal of this tutorial is to introduce group recommendation and group modeling to the UMAP audience. First we will introduce the problem of making recommendations to groups and adapting to groups, and give an overview of the state-of-the art approaches to group recommendation. Next, we will also analyze more challenging topics, such as including different behavioral aspects into group modeling, and evaluation of group recommendations. Throughout, hands-on activities will be included. The tutorial will conclude with a summary of challenges and open issues.

In this paper we present the results of a user study focusing on social relationships within small groups. The goal is to better understand how to incorporate the information about social relationships in group recommendation models. Our analysis, conducted on a data set of 150 participants in 41 groups deciding on a travel destination to visit together, brings out some intriguing outcomes. We demonstrate that social centrality is hardly an indicator of the social influence in the decision-making process of "equality matching" types of groups. However, socially central group members and socially close groups are significantly happier with group decisions than those who are loosely related. Moreover, in this paper we show that social relationships are indicators of other concepts relevant in group settings, therefore in group recommender systems as well.

Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating these preferences. Both empirical studies and theoretical frameworks have tried to identify the most effective preference aggregation techniques without coming to definite conclusions. In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain). We observe several individual and group properties and correlate them to choice satisfaction. Supported by these initial results we therefore advocate for the development of new group recommendation techniques that consider group dynamics and support the full group decision making process.

This PhD research aims to integrate group decision making into a personality based recommender systems in a domain with complex and emotional products i.e., e-tourism domain. In this domain, decisions, especially in groups, are often non rational. Based on the ongoing research on picture-based recommender systems at the e-commerce group, TU Wien and the software of Pixtri OG, the research will develop new methods to model group recommendations and support emotion-aware group decision processes, based on and evaluated by a world-wide study.