While most recommender systems cater to individual users’ needs, there are numerous situations where these systems are needed to meet groups’ demands. These systems are broadly labelled as Group Recommender Systems (GRSys). Traits like interpersonal relationships, group mood, and emotional contagion are essential to fulfilling the group’s needs. However, the group’s characteristics are frequently ill-defined and dynamic and are typically absent from systems modeling. Moreover, GRSys must maneuver between the needs of the group and the individuals when opinions differ and can contradict each other. The third edition of GMAP proposes consolidating a community of scholars interested in group modeling, adaptation, and personalization. Through the workshop, researchers continue their examination of the difficulties and possibilities of creating efficient procedures and instruments to facilitate collective decision-making. GMAP 2024 offered this unique opportunity to gather scholars from different fields to enrich discussions over GRSys’ research. The workshop also allowed attendees to strengthen their networks and establish new connections conducive to cutting-edge collaborative research.

Although most existing recommender systems support single users, there are many scenarios where these systems target the needs of groups. Traits such as group mood, emotional contagion, and interpersonal relationships are often ill-defined characteristics, tend to mutate over time, and are usually missing from the systems’ modeling, even though they play an indispensable part in group modeling. Furthermore, producing timely and accurate recommendations for groups that are explainable, fair, and privacy-protecting is a notoriously tricky challenge since group members may have divergent views and needs. The second GMAP workshop aims at bringing together a community of researchers focused on group modeling, adaptation, and personalization. The objective is to explore the challenges and opportunities of developing effective methods and tools to support group decision-making. The workshop, we brought together researchers from several disciplines, including Psychology, Computer Science, and Organizational Behavior, to discuss their latest research and ideas on this topic. It also provided opportunities for participants to share their research and experiences and to collaborate and network with other researchers in this field. The long-term goal is to foster a vibrant and inclusive community of researchers committed to advancing our understanding of group modeling, adaptation, and personalization by bringing together experts from different disciplines and perspectives. Throughout this workshop, we aim to identify critical challenges and opportunities in this area and develop a shared research agenda to guide future work.

Group modeling adaptation and personalization is an area explored in parallel by two different research communities. On the one hand, the user modeling community focuses on the preferences aggregation problem: how to combine preferences of individuals in a group so as to personalize, adapt, and explain content for this group to consume or experience? On the other hand, the computer-supported collaboration community focuses on the group formation problem: how to construct a group that will work together efficiently to solve a particular task? This area becomes increasingly significant as work becomes more flexible, online, and distributed. The connecting tissue between both communities is the urgent need to design algorithms, whether for recommending group content or group formations, that steer away from top-down algorithmic decision-making, which has proven to stifle user agency and create power inequalities between users and algorithms. The aim of the workshop is, for the first time, to bring together the two communities working on the two sides of Group Recommendations, with an overall goal to rethink group recommendation and shift paradigms from the current algorithm-centric to a user- and group-centric focus.

As the volume and complexity of distributed online work increases, collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of online collaborations by grouping workers in a top-down fashion and according to a set of predefined decision criteria. This approach often means that workers have little say in the collaboration formation process. Depriving users of control over whom they will work with can stifle creativity and initiative-taking, increase psychological discomfort, and, overall, result in less-than-optimal collaboration results—especially when the task concerned is open-ended, creative, and complex. In this work, we propose an alternative model, called Self-Organizing Pairs (SOPs), which relies on the crowd of online workers themselves to organize into effective work dyads. Supported but not guided by an algorithm, SOPs are a new human-centered computational structure, which enables participants to control, correct, and guide the output of their collaboration as a collective. Experimental results, comparing SOPs to two benchmarks that do not allow user agency, and on an iterative task of fictional story writing, reveal that participants in the SOPs condition produce creative outcomes of higher quality, and report higher satisfaction with their collaboration. Finally, we find that similarly to machine learning-based self-organization, human SOPs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible collaborators.

Forming teams of learners is a task that presents numerous challenges for educators increasingly relying on automated tools to optimize the process. The problem increases in difficulty in online classroom settings, where educators have little familiarity with the students. In this work, we present a User as Wizard study where 108 online crowd participants formed four teams of three teammates each from a pool of twelve dummy learner profiles. The profiles contained information about the learners’ Conscientiousness, Openness, and cognitive ability levels. These attributes were derived from a pre-study with a smaller sample of crowd participants (N=52) rating the relevance of the Big Five personality traits and cognitive ability in team formation for educational purposes. The User as Wizard study shows that most people tend to form within (meaning most attributes of the teammates even out) and between (meaning the teams have similar attributes averages) balanced teams. It also shows that people perceive Conscientiousness and Openness as two of the most relevant personality traits when profiling learners for team formation. We compare these results to the probability of them being random and discuss the findings in the light of human-centered modeling of system designs and automation in education.

Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.

In recent years, the notion of the Metaverse has become the focus of a growing body of work in the industry. However, there is no consensus on the conceptualization in academia. To date, much of this attention has revolved around technological challenges. However, what is notably missing from these discussions is a consideration of the human factors and social aspects that are considered more critical challenges within HCI. The aims of this SIG are as follows: Firstly, to provide a platform for researchers and practitioners to engage with the various definitions and the ways in which the Metaverse is developing. Secondly, to discuss the opportunities, challenges, and future possibilities in the context of HCI. This will lay the foundations to build a network for academics interested in the field for future multidisciplinary research relating to the Metaverse.

Critical, time-bounded, and high-stress tasks, like incident response, have often been solved by teams that are cohesive, adaptable, and prepared. Although a fair share of the literature has explored the effect of personality on various other types of teams and tasks, little is known about how it contributes to teamwork when teams of strangers have to cooperate ad-hoc, fast, and efficiently. This study explores the dynamics between 120 crowd participants paired into 60 virtual dyads and their collaboration outcome during the execution of a high-pressure, time-bound task. Results show that the personality trait of Openness to experience may impact team performance with teams with higher minimum levels of Openness more likely to defuse the bomb on time. An analysis of communication patterns suggests that winners made more use of action and response statements. The team role was linked to the individual's preference of certain communication patterns and related to their perception of the collaboration quality. Highly agreeable individuals seemed to cope better with losing, and individuals in teams heterogeneous in Conscientiousness seemed to feel better about collaboration quality. Our results also suggest there may be some impact of gender on performance. As this study was exploratory in nature, follow-on studies are needed to confirm these results. We discuss how these findings can help the development of AI systems to aid the formation and support of crowdsourced remote emergency teams.

Self-assembling team formation systems, where online users can select their teammates, are gaining research and industry interest. Still, the benefits of diversity remain frequently untapped for these teams, as people tend to choose others similar to them. In this study, we examine whether making users aware of the team’s diversity can impact their selections. In a study involving 120 crowd participants, working on the scenario of a crowdsourced innovation project, we tested the effects of two choice architecture and nudging techniques. The first technique displayed explicit personalized diversity information in the form of the current team diversity score and diversity recommendations. The second technique used diversity priming, in the form of counter-stereotypes and all-inclusive multiculturalism. Our results indicate that, while priming deterred participants from picking teammates of different regions, displaying diversity information was the only factor to positively enhance diverse choices. These results were not moderated by the users’ ’‘need to belong” levels, an intrinsic motivation justifying one’s need to form social ties. Other factors which we also find to predict selection behavior were the participants’ region of origin, participants’ gender, teammates’ functional backgrounds, and teammates’ order of appearance. In light of these findings, we suggest that nudging techniques need to be cautiously applied to online team formation as the different techniques differ in their ability to evoke diversity among intrinsically diverse crowds, and that personalised displaying of diversity information seems most promising.

In current youth-care programs, children with needs (mental health, family issues, learning disabilities, and autism) receive support from youth and family experts as one-to-one assistance at schools or hospitals. Occasionally, social robots have featured in such settings as support roles in a one-to-one interaction with the child. In this paper, we suggest the development of a symbiotic framework for real-time Emotional Support (ES) with social robots Knowledge Graphs (KG). By augmenting a domain-specific corpus from the literature on ES for children (between the age of 8 and 12) and providing scenario-driven context including the history of events, we suggest developing an experimental knowledge-aware ES framework. The framework both guides the social robot in providing ES statements to the child and assists the expert in tracking and interpreting the child's emotional state and related events over time.

People are increasingly making use of professional online services to outsource projects and remote teams through crowdsourcing tools. In these online self-assembly team formation settings, profiling information is vital to crowd users; it is focal to gathering knowledge about others and constructing virtual identities of themselves. This study investigates crowd users' preferences of profiling attributes displayed on online team formation systems. To evaluate participants' preferences, we looked at their willingness to see and their perceived usefulness across several profiling attributes grouped into surface-level and deep-level traits. These are classes of profiling traits quantifying group diversity that we use to distinguish between surface-level demographic and deep-level attitudinal traits. A total of 117 crowd participants answered close-ended questions about their preferences of profiling information concerning theirs and other crowd users' profiles on team self-assembly systems. Results indicate that crowd users significantly prefer disclosing surface-level attributes, both for themselves and others in terms of willingness to see and perceived usefulness. Surface-level attributes such as age, education, and availability are favored over deep-level attributes concerning crowd users' mental states, beliefs, and political affiliation. These results lay the foundation for future studies on the effectiveness and appropriateness of user-centered profiling information displayed on online crowdsourcing team formation systems.

As the volume and complexity of distributed online work increases, the collaboration among people who have never worked together in the past is becoming increasingly necessary. Recent research has proposed algorithms to maximize the performance of such teams by grouping workers according to a set of predefined decision criteria. This approach micro-manages workers, who have no say in the team formation process. Depriving users of control over who they will work with stifles creativity, causes psychological discomfort and results in less-than-optimal collaboration results. In this work, we propose an alternative model, called Self-Organizing Teams (SOTs), which relies on the crowd of online workers itself to organize into effective teams. Supported but not guided by an algorithm, SOTs are a new human-centered computational structure, which enables participants to control, correct and guide the output of their collaboration as a collective. Experimental results, comparing SOTs to two benchmarks that do not offer user agency over the collaboration, reveal that participants in the SOTs condition produce results of higher quality and report higher teamwork satisfaction. We also find that, similarly to machine learning-based self-organization, human SOTs exhibit emergent collective properties, including the presence of an objective function and the tendency to form more distinct clusters of compatible teammates.

Intangible Cultural Heritage is at a continuous risk of extinction. Where historical artefacts engine the machinery of intercontinental mass-tourism, socio-technical changes are reshaping the anthropomorphic landscapes everywhere on the globe, at an unprecedented rate. There is an increasing urge to tap into the hidden semantics and the anecdotes surrounding people, memories and places. The vast cultural knowledge made of testimony, oral history and traditions constitutes a rich cultural ontology tying together human beings, times, and situations. Altogether, these complex, multidimensional features make the task of data-mapping of intangible cultural heritage a problem of sustainability and preservation. This paper addresses a suggested route for conceiving, designing and appraising a digital framework intended to support the conservation of the intangible experience, from a user and a collective-centred perspective. The framework is designed to help capture the intangible cultural value of all places exhibiting cultural-historical significance, supported by an extensive analysis of the literature. We present a set of design recommendations for designing mobile apps that are intended to converge crowdsourcing to Intangible Cultural Heritage.

Whether it's for altruistic reasons, personal gains or third party's interests, users are influenced by different kinds of motivations when making use of mobile geo-crowdsourcing applications (geoCAs). These reasons, extrinsic and/or intrinsic, must be factored in when evaluating the use intention of these applications and how effective they are. A functional geoCA, particularly if designed for Volunteered Geographic Information (VGI), is the one that persuades and engages its users, by accounting for their diversity of needs across a period of time. This paper explores a number of proven and novel motivational factors destined for the preservation and collection of Intangible Cultural Heritage (ICH) through geoCAs. By providing an overview of personalisation research and digital behaviour interventions for geo-crowdsoured ICH, the paper examines the most relevant usability and trigger factors for different crowd users, supported by a range of technology-based principles. In addition, we present the case of StoryBee, a mobile geoCA designed for "crafting stories'' by collecting and sharing users' generated content based on their location and favourite places. We conclude with an open-ended discussion about the ongoing challenges and opportunities arising from the deployment of geoCAs for ICH.