Gender representation still frequently falls short in video games, with titles often portraying stereotypical roles, under-representing female characters, or lacking diverse gender identities. The present research explored diversity in facial expressions based on the gender of game characters and players across League of Legends, Dota 2, Overwatch, and Valorant. We recruited a total of 99 participants for an online study. We presented approximately 20 game characters to each participant and asked them to discern the predominant emotional expression (neutral, happiness, anger, disgust, fear, sadness, surprise, alluring, or determined). They further rated the emotional intensity, followed by indicating their familiarity with the presented game characters. We found that the emotional expressions of the game characters portray gender stereotypes in the design such as anger mainly being expressed by male characters, while female characters are more likely to be presented as alluring. Greater familiarity was associated with more specific readings, whereas less familiarity results in ascribing more stereotypical emotions. Our findings are discussed with implications for game designers and the gaming industry in mind.

Public transport disruptions require travellers to make rapid replanning decisions under uncertainty, increasing cognitive burden and negatively affecting the travel experience. Supporting such decisions through personalised, context-aware recommendations requires modelling heterogeneous and context-dependent behaviour. This paper investigates how such behaviour can be modelled for route recommendations during disruptions. Using a discrete choice experiment and latent class modelling, we identify recurring decision strategies - context-adaptive, efficiency-driven rerouting, and waiting-oriented - rather than stable traveller types. Overlap between classes suggests that individuals shift strategies across situations. By framing latent classes as decision strategies, this work provides modelling insights beyond the public transport domain and positions latent class modelling as a structured step toward personalisation in context-dependent decision settings.

Recommendation algorithms support decision-making, yet their reasoning is often opaque. Explanations can improve user understanding, but the role of tone has received little attention, particularly in relation to situational context. This is especially relevant for route recommendations, where factors such as urgency shape travellers’ needs. Using a mixed-method approach (focus groups, n = 15; validation study, n = 32; online experiment, n = 150), we examine how tone is perceived across contextual conditions in public transport. Unlike prior work comparing tone across domains and users, we focus on within-domain, situational context-dependent effects. Results show that situational context affects tone preferences. In urgent situations, humorous and empathetic tones are less appreciated than neutral or authoritative ones, and some tones are more context-sensitive than others. Our findings emphasise considering situational context when designing recommendation explanations, and offer guidance for designers and researchers.

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.

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.

Physical activity is entering the virtual realm. Zwift is an at-home cycling system that is enjoying increasing popularity, yet the specifics of the experience of a virtual cyclist have not been studied to date. Building virtual sports systems can make physical activity accessible to more diverse user groups. To understand how and why users engage in virtual cycling, we conducted n=22 interviews with Zwift users. Through charting the motivations behind using Zwift, we determined that it allowed users to engage in a range of cycling activities traditionally reserved for professional cyclists. Our work reports on key motivations and identifies five key strategies which Zwift uses to create an engaging virtual sports experience. Further, we discuss how Zwift creates a world of virtual professionalism. Our findings offer a structured understanding of the experience of Zwift which can be used to inspire the design of future virtual amateur sports systems.

We are proud to present the following three keynote speakers, who will share their expertise with the participants of ACM UMAP 2021, the 29th Conference on User Modeling, Adaptation and Personalization. In this article, you find the speakers’ biographies and the titles of their keynote talks.

Adaptive systems are usually interactive systems. As such they stand to benefit considerably from a development lifecycle that ensures user involvement from the early design stages, and embraces evaluation, in both formative and summative forms. Evaluating an adaptive system involves a number of specific problems and pitfalls that need to be addressed by the selection of specific methods, techniques and criteria. This tutorial aims to introduce participants to the peculiarities that arise when evaluating adaptive interactive systems. A layered evaluation framework is used to separate the evaluation process into a number of different aspects which can be applied at different stages throughout the development life-cycle.

Researchers claim that we are facing a global loneliness epidemic, and that mental illness, anxiety disorders, stress and burnout are on the rise. Technology, such as social media, is often found to have a detrimental effect on mental health, self-esteem and sleep, and to cause anxiety and feelings of loneliness. This talk is about how adaptive systems can actively improve well-being, instead of contributing to making it worse. We will discuss different ways of doing so, the work already done, the challenges faced, and our vision of a new kind of personalized systems that act as guardian angels. First, systems can provide emotional support, adapted to the recipient's characteristics such as their personality, affective state, cultural background, and stressors experienced. Second, systems can aid humans to provide emotional support. People often struggle to support others, and may say something that is counter productive or nothing at all. Systems can train people on how to provide support. They can also mediate emotional support, adapting support messages to both the support giver and recipient, taking into account for example the closeness of relationships and people's personality. Third, systems can support and motivate people to adopt behaviours that improve their well-being and that of others, and to better regulate their emotions. There has been much research on persuasive technology to support people in changing behaviours, and it has been shown that both the behaviour change techniques used, and attributes of techniques need adapting. Whilst much persuasive technology research has focused on physical well-being and sustainability, the emphasis in this presentation will be on mental well-being and encouraging people to help each other. Fourth, systems can team people up. Systems can decide who are best placed to provide support and motivation, encouraging particular people to support (or ask help from) particular other people. Additionally, adaptive group formation (or peer-to-peer recommendations) can be used for joint problem solving scenarios, with a system deciding or recommending who should work with whom. There are many benefits to group work, but it is also often a source of negative emotions. Adaptive group formation can consider affect and personality in addition to expertise, to minimize such negative emotions. Finally, systems can improve the well-being of groups and not just individuals. People's well-being is influenced by the well-being of others in their surroundings, and people's actions impact the well-being of others. Systems can monitor group well-being. They can encourage and support effective group behaviours, for example, by providing feedback on how group members and the group as a whole function. They can support the building of group identity and cohesion. They can support groups in making decisions that are good for group well-being. Overall, we envision adaptive systems as effective and emotionally intelligent contributors in the community, improving the way people interact, and acting like guardian angels.

This paper investigates adaptation of feedback to learners' cultural backgrounds. First, we investigate how to portray the cultural background of a learner. Second, we present a qualitative focus-group study, investigating how participants from different cultures believe culture affects the kind of feedback given to a learner. Finally, we present an empirical study on how humans adapt feedback based on the cultural background of learners to inspire an algorithm. Our investigations resulted in a set of stories which can be used to reliably portray a person's culture when investigating cultural adaptation in indirect experiments and user as wizard studies. They also provided insights into the adaptations people make to cultural differences.

Personality impacts all areas of our lives; it governs who we are and how we react to life’s challenges. Personalized systems that adapt to end users should take into account the user’s personality to perform well. Several methodologies (e.g. User-as-Wizard, indirect studies) that use personality adaptation require first for personality to be conveyed to the participant; this has few validated approaches. Furthermore, measuring personality is often time consuming, prone to response bias (e.g. using questionnaires) or data intensive (e.g. using behaviour or text mining). This paper presents a methodology for creating and validating stories to convey psychological traits and for using such stories with a personality slider scale to measure these traits. We present the validation of the scale and evaluate its reliability. To evidence the validity of the methodology, we outline studies where the stories and scale have been effectively applied (in recommender systems, intelligent tutoring systems, and persuasive systems).

Intentional engagement in positive activities, such as practicing kindness, showing generosity or expressing gratitude, can help people increase their happiness levels and improve their wellbeing. In this paper we explore how a gamified digital behaviour change intervention can be adapted to encourage people of different personality types to engage in simple acts of kindness. Participants were assigned 5 daily activities for 7 days, and asked to complete as many as possible by the end of each day. Participants received different persuasive notifications every day to encourage them to complete a higher number of activities. We investigated how participation levels are influenced by different personality types, different persuasive message types and different categories of activities. Furthermore, we analysed the influence of the intervention on participant behaviour and the effect on behavioural intention, by comparing pre-intention and post-intention to perform different kinds of positive activities. The findings from this study have implications for future work on personalising persuasive interventions to improve wellbeing and prevent mental health problems.

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.

Recommender systems have been widely applied in the literature to suggest individual items to users. In this paper, we consider the harder problem of package recommendation, where items are recommended together as a package. We focus on the clothing domain, where a package recommendation involves a combination of a 'top' (e.g. a shirt) and a 'bottom' (e.g. a pair of trousers). The novelty in this work is that we combined matrix factorisation methods for collaborative filtering with hand-crafted and learnt fashion constraints on combining item features such as colour, formality and patterns. Finally, to better understand where the algorithms are underperforming, we conducted focus groups, which lead to deeper insights into how to use constraints to improve package recommendation in this domain.

Recent studies on recommender systems raise attention to the importance of context, intended both as the external environment and even as the user's internal state, such as, for example, the mood in which the users are going to perform the recommended activities. This is a key factor also in the group recommendation domain, where the context is characterized by the presence of other people with whom the activities must be performed. In this case, social influences and relationships between users come into play, and the individual satisfaction that each user will obtain could change in relation to those social characteristics. In this work, we start an experimental analysis on how ties' strength and possible conflicts in a relationship can influence the opinion shift, with the aim to derive a model that can be used to adapt individual utilities to the "Group Context" before aggregating them into the group's ones.

Adapting to learner characteristics is essential when selecting exercises for learners. This paper investigates how humans adapt next exercise selection to learner personality and invested mental effort to enable a future Intelligent Tutoring System to use these adaptations. Participants were presented with validated stories of a learner`s personality at polarised levels, a validated story conveying the mental effort invested in carrying out a given task and an indication of a previous performance (just passing) at a simple arithmetic exercise. Participants were also shown a selection of validated exercises of varying difficulty levels and asked to select the exercise which they thought the learner should do next. We found that overall more difficult exercises were selected for learners who used little effort than for learners who used more effort. We found that although an exercise of slightly harder difficulty remains the most popular choice in the high and low self-esteem conditions, for low self-esteem, participants picked an exercise of lower or the same difficulty more often than in the high condition.

Although computers could offer emotional support as well as task support when aiding a user for a complex task, there is little current understanding of how they might do this. Moreover existing demonstrations of emotional support, though promising, only cover a small number of types of support and investigate a limited number of algorithms designed by hand. In this paper, we present an empirical investigation that starts from first principles, determining different categories of stressors for which emotional support might be useful, different categories of emotional support utterances and promising algorithms for deciding the content and form of textual emotional support messages according to the stressors present. At each stage, the results are validated through empirical experiments with human participants who, for instance, are required to place statements into categories, evaluate possible support messages in different imagined situations and compose their own emotional support from options offered. This development methodology allows us to avoid potentially challenging ethical issues in presenting people with stressful situations. Although our algorithms are attempting to choose emotional support based on the general, “naive” competence of human speakers, we use as a running example situations that can arise when attending a medical emergency and awaiting expert help.

This paper investigates whether different types of persuasive reminder should be sent to patients with different personalities. We describe a study where we presented participants with a personality measure, then describe a scenario with a fictional patient, who has not performed a skin check for recurrent melanoma. We asked patients to imagine they are in that situation and rate validated reminders based on Cialdini's 6 principles of persuasion for their suitability. Participants then chose their favourite reminder, and an alternative reminder to send if that one failed. We found that persuasive reminders that use `Authority' and 'Liking' are the most popular overall. We also found that personality had an effect when deciding on the type of persuasive reminder to use. In particular, we have found that those with high emotional stability are more responsive to any kind of persuasion, those with low agreeableness rated all types of reminder higher than those with high, and that conscientiousness matters when selecting an alternative reminder.

Social isolation among older adults represents a significant societal challenge in which persuasion offers a potential solution. To develop a persuasive interactive system for this purpose, we conducted a modeling study with carers to discover how persuasion is used to encourage social interaction among older adults. From an analysis of the results, we identified and defined effective persuasive strategies grounded in theories of persuasion and developed a computational model for applying them. This article reports the findings from an evaluation of the generalizability of this model and presents a revised version based on these results. The article concludes with a discussion on possible domain-specific conceptual features between the model evaluated and the revised model developed.

Digital behaviour interventions aim to encourage and support people to change their behaviour, for their own or communal benefits. Personalization plays an important role in this, as the most effective persuasive and motivational strategies are likely to depend on user characteristics. This tutorial covers the role of personalization in behaviour change technology, and methods and techniques to design personalized behaviour change technology.

This article presents research conducted to establish how information is shared across the personal social network in the sensitive context of a health crisis. We worked with parents of very sick babies who were cared for in a hospital's Neonatal Unit (NNU). Through a combination of interviews, a focus group, and surveys, we developed a user model of the information that parents wanted to share, and how they adapted this information to individual recipients. We then developed a prototype software tool which created adaptive updates for members of the parents' social network. The updates contained summaries of large volumes of complex medical data about the baby, nonmedical information about the parents, and practical information about the hospital. Updates were automatically adapted to individual members of parents' social networks, based on our user model. The tool was evaluated in a large NNU in the United Kingdom with parents of babies who were currently being cared for in the unit. We found that parents adapted the information that they shared about themselves and their babies based on the emotional proximity of their network members. They gave most detail to those who were emotionally closest to them and least to those who were less close. Parents also adapted information content to the recipient's tendency to worry and empathize. Two adaptive strategies were deployed by parents, (a) benign deceit—not telling the whole truth—and (b) promotion of empathetic members of the social network to a higher level of emotional proximity, so that they were given more information. We generated a number of directions for future work, and issues to consider around designing adaptive mediated communications systems for sensitive contexts. These include the potential to generalize our model to other medical contexts and considerations to apply when deliberately designing deceit into adaptive systems.

Community First Responders are often the first on the scene in rural medical emergencies, and experience situations where stress is inherent. One way of reducing this stress is through the use of an Empathic Conversational Agent which can provide Emotional Support, and be applied to systems designed for the pre-hospital care domain. This paper outlines the groundwork for this goal by describing the development of stories which describe a particular stressor, and validated Emotional Support categories and statements. These will be used when developing an algorithm for adapting Emotional Support to different stressful situations. We identified 5 categories of Emotional Support, and a set of 52 statements which were validated as belonging to these categories. We then present a preliminary analysis of the patterns of usage of Emotional Support categories for each of the stressors.

This paper describes the development of an algorithm for use by an Empathic Conversational Agent for choosing appropriate emotional support messages to a learner receiving feedback on their performance. We present a study where we employed a User as Wizard approach to explore how such statements were used by people giving feedback to a fictional learner with high or low conscientiousness and varying grades. We found that the type of emotional support employed depended primarily on the grade that had been achieved, but conscientiousness also influenced the amount of advice given. Interesting differences were found in how people combine Emotional Support messages depending on grade and level of conscientiousness, which inspired an algorithm for automatically choosing appropriate messages depending on context.

In this paper we design and implement an artificial task-based scenario that seeks to induce loneliness and acute stress. We explore how the presence of a virtual teammate called Mary and the differing types of support that she provides affects users stress during a task. We investigate how empathic support, task support and the combination of task and empathic support affect stress. Stress is measured using both physiological sensors (skin conductance and heart rate) and self-reporting questionnaires. The results obtained offer insight into the best type of support to give individuals taking part in critical situations, such as in our chosen domain of pre-hospital care. This work lays down the foundations for future work in the development of an intelligent algorithm for a virtual teammate which can provide benefits to both casualty and carer welfare.

This paper describes how a set of stories, each conveying a personality trait from the Five Factor Model at a high or low level, were developed using Amazon's Mechanical Turk. These stories will be used to develop interfaces that adapt to personality using a User as Wizard method. The paper shows how difficult it is to construct stories that convey a single personality trait. It also shows how such stories can be constructed for most cases, and how Mechanical Turk can aid to achieve this.

This book is an edited collection of chapters on artificial companions (ACs) resulting from a workshop.It purports to discuss the philosophical and ethical issues associated with ACs, what ACs should be like and how to construct them, and to provide examples of special-purpose ACs.Table 1 shows the chapters of the book and their respective authors.When I bought a vacuum-cleaning robot called Roomba, it never occurred to me that this was actually a companion, as claimed by Peltu and Wilks in this book's afterword.Yes, it independently helps me, autonomously recharges its battery, and occasionally talks: "Clean brushes."I had higher expectations of a companion, however.Perhaps it would feel more like a companion if it expressed disgust with my mess (cf.Bee et al.), inquired knowledgeably about my holidays and family (cf.Wilks), empathized with my feelings (cf.Bevacqua et al.), and advised scraping off spilled porridge with a spoon (cf.Sloman).There is no consensus in this book on what an AC should be like; each chapter tends to express an alternative (often quite radically different) opinion.In the foreword, Wilks defines ACs as "conversationalists or confidants" that get to know their owners, assist with Internet interactions, provide company and companionship, and build their owner's biography.However, when analyzing the necessary conditions for being an AC, Pulman concludes that conversation is not needed, and Boden strongly objects to ACs as confidants because of privacy concerns.Rather than providing company, according to O'Hara, ACs will represent (technophobe) owners in complex dealings with technology.Lowe takes this one step further and questions whether ACs are really others or whether they are cooler-headed versions of ourselves.Whereas Romano defines an AC as a good friend who, among other things, makes you laugh, shares your emotions, and listens, Sloman focuses on the (in his opinion) more difficult problem of creating ACs that provide help and assistance.Turkle sees ACs as offering care as well as being good company, teachers, and lovers (!).Taylor et al. deviate from the anthropomorphic view of ACs, and discuss a robot that has no human-like properties other than that it is fed on organic material.

This study examined how emotional proximity and gender affect people's information requirements when someone that they know is chronically or critically ill. In an online study, participants were asked what information they would want to receive about members of their social network in three categories: someone who was very close, someone who was not so close, and someone who was not close at all. Our results show that the information that people want can be predicted from their gender and emotional proximity to the network member. The closer the relationship with the patient, the more information people want. Women want more information than men. We propose a model for the socially intelligent communication of health information across the social network, and discuss areas for its application.

Experiencing emotional distress is the number one reason why people who are undergoing behaviour modification (e.g. quitting smoking, dieting) suffer from relapses. Providing emotional support is an effective way to help them overcome the unpleasant effects of negative affect and adhere to their regimen. Building computers with such ability has grabbed the attention of the HCI community in recent years. Early research has shown some promising results when adopting strategies of how we comfort others, but many questions on how to build such systems remain unanswered. This paper presents the results of a 2 (modality: animated vs. no visual) by 3 (intervention: non-empathy vs. empathy vs. empathy and expressivity) between-subjects study that investigates the impact of two important factors and their interaction in the design of such systems: (1) different ways of expressing empathy, and (2) the modality of delivering such content. Findings and implications for the design of empathic computer systems are discussed and directions for future research are suggested.

There is a vast amount of data associated with any one patient. It is challenging for medical staff to understand all this data. It is even harder for a lay person, who may not even know what medical terms mean. The research project BabyTalk-Clan aims to create personalized summaries of data for a lay audience. It uses sensitive, highly-detailed clinical data relating to a patient. This includes medication given, test results, notes made by medical staff, and continuous physiological signals such as heart rate. We took a qualitative approach to knowledge acquisition for user requirements. Using interviews and a focus group within a Grounded Theory methodology, we discovered that most lay users want only a very high-level summary of the baby's state. What lay users do want is information about how the parents are coping, and what support they need. Findings were cross-validated through a questionnaire.

This paper characterizes general properties of useful, or Effective, explanations of recommendations. It describes a methodology based on focus groups, in which we elicit what helps moviegoers decide whether or not they would like a movie. Our results highlight the importance of personalizing explanations to the individual user, as well as considering the source of recommendations, user mood, the effects of group viewing, and the effect of explanations on user expectations.

It is often desirable that referring expressions be chosen in such a way that their referents are easy to identify. This article focuses on referring expressions in hierarchically structured domains, exploring the hypothesis that referring expressions can be improved by including logically redundant information in them if this leads to a significant reduction in the amount of search that is needed to identify the referent. Generation algorithms are presented that implement this idea by including logically redundant information into the generated expression, in certain well-circumscribed situations. To test our hypotheses, and to assess the performance of our algorithms, two controlled experiments with human subjects were conducted. The first experiment confirms that human judges have a preference for logically redundant expressions in the cases where our model predicts this to be the case. The second experiment suggests that readers benefit from the kind of logical redundancy that our algorithms produce, as measured in terms of the effort needed to identify the referent of the expression.

Constraint diagrams (Kent, 1997) are a complex diagrammatic notation designed to express logical statements especially for use in software specification and reasoning. Not surprisingly, since this is an expressive language, there are some difficulties in reading the semantics of a diagram unambiguously. Some extra annotations (in the form of a reading tree) disambiguate the diagrams. However, this extra requirement (of drawing a reading tree) places a burden on the user. An attempt to remove the need for such a reading tree (or perhaps to automatically generate a reading tree, which could be altered by a user if they wished to) has been given via an algorithm to generate a default reading from the diagram. This algorithm is based on a number of principles - most of which are properties of the diagram. We wish to know whether these principles are intuitive and whether the default reading reflects a good proportion of users' intuitions, and we have performed a user-based study to test this.

Researchers have investigated the possibilities for supporting language learning through a range of technologies, most recently mobile phones and interactive television (iTV). Drawing on a focus group study, we present a scenario demonstrating an approach that blends the features of these two technologies. Three areas are identified for further exploration: pedagogy, technical feasibility and interaction design issues.

In order to optimally support learning, help should be given at an appropriate level: providing the users with new information, relevant to and needed for their task. This paper discusses the design and evaluation of such a help system, applied in the Radiology domain.