The COVID-19 pandemic has led to a surge in video-conferencing software usage in businesses, universities, and schools. However, research indicates that video-conferencing can hinder creative tasks that require social presence, immediacy, and equal contribution. To address these concerns, this paper advocates the use of analytical and feedback tools to minimize the negative effects of video-conferencing. Additionally, the paper examines how computer-based creative thinking training can improve divergent thinking abilities in teams. An exploratory study was conducted using a real-time visual support system that encouraged equal participation by visualizing contribution levels. The results demonstrated that the visual support system had a positive impact on creativity performance in teams during video-conferencing meetings. These findings add to the literature on digital technology’s ability to support team creativity.

With the advent of mixed reality devices such as the Microsoft HoloLens, developers have been faced with the challenge to utilize the third dimension in information visualization effectively. Research on stereoscopic devices has shown that three-dimensional representation can improve accuracy in specific tasks (e.g., network visualization). Yet, so far the field has remained mute on the underlying mechanism. Our study systematically investigates the differences in user perception between a regular monitor and a mixed reality device. In a real-life within-subject experiment in the field with twenty-eight investment bankers, we assessed subjective and objective task performance with two- and three-dimensional systems, respectively. We tested accuracy with regard to position, size, and color using single and combined tasks. Our results do not show a significant difference in accuracy between mixed-reality and standard 2D monitor visualizations.

Today more and more people use social networks and so the differences in personalities of users become more diversified. The same holds true for available news content. To test if regular news and fake news are distributed similarly and to what extent this depends on the personality and behavior of individuals, we conducted a mixed-method study. Through an online questionnaire we measured personality traits of individuals in social networks, how they behave, and how they are connected to each other. Using this data, we developed an agent-based model of an online social network. Using our model, an average of 92% of regular news and 98% of fake news were disseminated to the whole network. Network density turned out to be more important for dissemination than the differences in personality and behavior of individuals. Thus the spread of fake news can not only be addressed by focusing on the personality of individual users and their associated behavior. Systemic approaches---integrating both human and algorithm---must be considered to effectively combat fake news.

With the advent of artificial intelligence (AI) methods, smart decision support systems (DSSs) are becoming ubiquitous. Such systems help reduce complexity for operators by automating data integration tasks and recommending actions. However, these systems are sometimes flawed. It is not sufficiently understood whether, when and why operators comply with such systems in erroneous or correct cases. We empirically investigate compliance with correct and defective DSSs, the influence of correct and erroneous DSS's on performance and subjective factors related to compliance. In the study, a business game was used as an experimental setting in which 40 users took part. The impact of system correctness on user acceptance, trust, compliance and overall performance was investigated. The results show that the defective system reduces trust in automation (−47%), reduces usefulness (−58%), reduces acceptance (−62%) and reduces overall performance (−32%). Overall, the defective system was less user-friendly (−27%). Nevertheless, users who rated the system's usability higher, outperformed users who rated it lower. Usability is therefore an intermediary that compensates for the negative influence of erroneous DSSs.

The use of recommendation systems is widespread in online commerce. Depending on the algorithm that is used in the recommender system different types of data are recorded from user interactions. Typically, better recommendations are achieved when more detailed data about the user and product is available. However, users are often unaware of what data is stored and how it is used in recommendation. In a survey study with 197 participants we introduced different recommendation techniques (collaborative filtering, content-based recommendation, trust-based and social recommendation) to the users and asked participants to rate what type of algorithm should be used for what type of product category (books, mobile phones, contraceptives). We found different patterns of preferences for different product categories. The more sensitive the product the higher the preference for content-based filtering approaches that could work without storing personal data. Trust-based and social approaches utilizing data from social media were generally rejected.

In virtual environments, the space that can be explored by real walking is limited by the size of the tracked area. To enable unimpeded walking through large virtual spaces in small real-world surroundings, redirection techniques are used. These unnoticeably manipulate the user's virtual walking trajectory. It is important to know how strongly such techniques can be applied without the user noticing the manipulation-or getting cybersick. Previously, this was estimated by measuring a detection threshold (DT) in highly-controlled psychophysical studies, which experimentally isolate the effect but do not aim for perceived immersion in the context of VR applications. While these studies suggest that only relatively low degrees of manipulation are tolerable, we claim that, besides establishing detection thresholds, it is important to know when the user's immersion breaks. We hypothesize that the degree of unnoticed manipulation is significantly different from the detection threshold when the user is immersed in a task. We conducted three studies: a) to devise an experimental paradigm to measure the threshold of limited immersion (TLI), b) to measure the TLI for slowly decreasing and increasing rotation gains, and c) to establish a baseline of cybersickness for our experimental setup. For rotation gains greater than 1.0, we found that immersion breaks quite late after the gain is detectable. However, for gains lesser than 1.0, some users reported a break of immersion even before established detection thresholds were reached. Apparently, the developed metric measures an additional quality of user experience. This article contributes to the development of effective spatial compression methods by utilizing the break of immersion as a benchmark for redirection techniques.

We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that-under certain circumstances-such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.

The 2017 Workshop on Health Recommender Systems was held in conjunction with the 2017 ACM Conference on Recommender Systems in Como, Italy. Following the fists workshop in 2016, the focus of this workshop was on enhancing the results of the first workshop by elaborating discussions on the topics, attracting scientist from other domains, finding cross-domain collaboration, and establishing shared infrastructures.

How can you discover something new, that matches your interest? Recommender Systems have been studied since the 90ies. Their benefit comes from guiding a user through the density of the information jungle to useful knowledge clearings. Early research on recommender systems focuses on algorithms and their evaluation to improve recommendation accuracy using F-measures and other methodologies from signal-detection theory. Present research includes other aspects such as human factors that affect the user experience and interactive visualization techniques to support transparency of results and user control. In this paper, we analyze all publications on recommender systems from the scopus database, and particularly also papers with such an HCI focus. Based on an analysis of these papers, future topics for recommender systems research are identified, which include more advanced support for user control, adaptive interfaces, affective computing and applications in high risk domains.