To improve decision-making and prevent information overload, music streaming services try to offer users personalized content. Vocabulary can be an important component in achieving personalization, in addition to recommender systems that ensure users are given access to their musical preferences. One way to apply personalization through vocabulary is to use personal pronouns. However, it is still unclear how the use of vocabulary personalization affects users’ decisions and expectations when it comes to music streaming services. This study aims to explore how vocabulary framing in playlist titles influences decision-making and the content expectations of the music playlist. Despite challenges in making informed vocabulary choices, our results show a substantial impact of personalized vocabulary in playlist titles on user choice and overall expectations. Users distinctly favor playlists with personalized vocabulary, emphasizing their crucial role in enhancing the music streaming experience.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the "human-machine co-existence"and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the ninth edition of HAAPIE includes 4 long papers and 2 short papers.

Popularity bias in recommendation lists refers to over-representation of popular content and is a challenge for many recommendation algorithms. Previous research has suggested several offline metrics to quantify popularity bias, which commonly relate the popularity of items in users’ recommendation lists to the popularity of items in their interaction history. Discrepancies between these two factors are referred to as popularity miscalibration. While popularity metrics provide a straightforward and well-defined means to measure popularity bias, it is unknown whether they actually reflect users’ perception of popularity bias.

To address this research gap, we conduct a crowd-sourced user study on Prolific, involving 56 participants, to (1) investigate whether the level of perceived popularity miscalibration differs between common recommendation algorithms, (2) assess the correlation between perceived popularity miscalibration and its corresponding quantification according to a common offline metric. We conduct our study in a well-defined and important domain, namely music recommendation using the standardized LFM-2b dataset, and quantify popularity miscalibration of five recommendation algorithms by utilizing Jensen-Shannon distance (JSD). Challenging the findings of previous studies, we observe that users generally do perceive significant differences in terms of popularity bias between algorithms if this bias is framed as popularity miscalibration. In addition, JSD correlates moderately with users’ perception of popularity, but not with their perception of unpopularity.

Active mobility, such as biking, faces a common challenge in Swedish municipalities due to the lack of adequate lighting during the dark winter months. Insufficient lighting infrastructure hinders individuals from choosing bicycles, despite the presence of well-maintained bike paths and a willingness to cycle. To address this issue, a project has been undertaken in the Swedish municipality of Skara for an alternative lighting solution using drones. A series of tests have been conducted based on drone prototypes developed for the selected bike paths. Participants were invited to cycle in darkness illuminated by drone lighting and share their mobility preferences and perception. This paper summarizes the users’ perception of drone lighting as an alternative to fixed lighting on bike paths, with a special focus on the impact on travel habits and the perceived sense of security and comfort. Most participants were regular cyclists who cited bad weather, time, and darkness as significant factors that deterred them from using bicycles more frequently, reducing their sense of security. With drone lighting, the participants appreciated the illumination’s moonlight-like quality and its ability to enhance their sense of security by illuminating the surroundings. On the technology side, they gave feedback on reducing the drone’s sound and addressing lighting stability issues. In summary, the test results showcase the potential of drone lighting as a viable alternative to traditional fixed lighting infrastructure, offering improved traffic safety, sense of security, and comfort. The results show the feasibility and effectiveness of this innovative approach, supporting transformation towards active and sustainable mobility, particularly in regions facing lighting challenges.

Nowadays, the profound digital transformation has upgraded the role of the computational system into an intelligent multidimensional communication medium that creates new opportunities, competencies, models and processes. The need for human-centered adaptation and personalization is even more recognizable since it can offer hybrid solutions that could adequately support the rising multi-purpose goals, needs, requirements, activities and interactions of users. The HAAPIE workshop1 embraces the essence of the "human-machine co-existence"and brings together researchers and practitioners from different disciplines to present and discuss a wide spectrum of related challenges, approaches and solutions. In this respect, the seventh edition of HAAPIE includes 2 long papers and 5 short papers.

Recommender systems are designed to help us navigate through an abundance of online content. Collaborative filtering (CF) approaches are commonly used to leverage behaviors of others with a similar taste to make predictions for the target user. However, CF is prone to introduce or amplify popularity bias in which popular (often consumed or highly ranked) items are prioritized over less popular items. Many computational metrics of popularity biases — and resulting algorithmic (un)fairness — have been presented. However, it is largely unclear whether these metrics reflect human perception of bias and fairness. We conducted a user study with 170 participants to explore how users perceive recommendation lists created by algorithms with different degrees of popularity bias. Our results show — surprisingly — that popularity biases in recommendation lists are barely observed by users, even when corresponding bias/fairness metrics clearly indicate them. 

In recent years, the integration of physical and cyber spaces has rapidly progressed. Sensing data from various sources is increasingly being fed back into physical space, and there are increasing cases of sensing and utilizing human biosignal information and emotions as well. This study investigates the psychological effects of visualizing and sharing people’s emotions and heart rate through two case studies. The first case study examines the psychological effects and usefulness of sharing others’ emotion and heart rate by displaying them beside he or she in physical space using a prototype of an augmented reality application called “Emo-Space”. The second case study proposes chat systems called “Emo-Circle” and “Heart-View” that allow for the visualization and sharing of emotions and heart rate and examines the effects of using the system on interpersonal relationships and self-awareness of emotions. We provide insights from the two case studies into that how such data of emotions and heart rate data indicating the physical and mental states of individuals can be applied to daily lives, including communication with others.

Music recommender systems are a widely adopted application of personalized systems and interfaces. By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (contentbased methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods) or a mixture of these with other features (hybrid methods). In this chapter, we focus on the listener’s aspects of music recommender systems. We discuss different factors influencing relevance for recommendation on both the listener’s and the music’s side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i. e., personality traits and demographic characteristics, and cultural features, i. e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior and (iii) listener intention, in particular by studying music information behavior, i. e., how people seek, find and use music information. This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.

Information retrieval and recommender systems are deployed in real world environments. Therefore, to get a real feeling for the system, we should study their characteristics in “real world studies”. This raises the question: What does it mean for a study to be realistic? Does it mean the user has to be a real user of the system or can anyone participate in a study of the system? Does it mean the system needs to be perceived as realistic by the user? Does it mean the manipulations need to be perceived as realistic by the user?

Various social influences affect group decision-making processes. For instance, individuals may adapt their behavior to fit in with the group's majority opinion. Furthermore, ingroup favoritism may lead individuals to favor the ideas of ingroup members rather than the outgroup. So far, little is explored on how these phenomena of social conformity and ingroup favoritism manifest in group decision-making processes when a group has to decide in favor or against an item. We address such a scenario where the 'flipping direction' of conformity (in favor or against an item) matters. Specifically, we explore whether and how the ingroup favoritism manifests differently in terms of conformity behavior depending on the 'flipping direction'. The results show that group inclusiveness does not play a role in the general tendency to conform. However, when it comes to a negative flipping direction, a higher feeling of group inclusiveness seems to play a role; yet, for individualist cultures only.