User Modeling and User-Adapted Interaction (UMUAI) provides an interdisciplinary forum for the dissemination of new research results on interactive computer systems that can be adapted or adapt themselves to their current users, and on the role of user models in the adaptation process.

UMUAI has been published since 1991 by Kluwer Academic Publishers (now merged with Springer Verlag).

UMUAI homepage with description of the scope of the journal and instructions for authors.

Springer UMUAI page with online access to the papers.

Latest Results for User Modeling and User-Adapted Interaction

25 May 2022

The latest content available from Springer
  • Empathy as an engaging strategy in social robotics: a pilot study


    Empathy plays a fundamental role in building relationships. To foster close relationships and lasting engagements between humans and robots, empathy models can provide direct clues into how it can be done. In this study, we focus on capturing in a quantitative way indicators of early empathy realization between a human and a robot using a process that encompasses affective attachment, trust, expectations and reflecting on the other’s perspective within a set of collaborative strategies. We hypothesize that an active collaboration strategy is conducive to a more meaningful and purposeful engagement of realizing empathy between a human and a robot compared to a passive one. With a deliberate design, the interaction with the robot was presented as a maze game where a human and a robot must collaborate in order to reach the goal using two strategies: one maintaining control individually taking turns (passive strategy) and the other one where both must agree on their next move based on reflection and argumentation. Quantitative and qualitative analysis of the pilot study confirmed that a general sense of closeness with the robot was perceived when applying the active strategy. Regarding the specific indicators of empathy realization: (1) affective attachment, affective emulation was equally present throughout the experiment in both conditions, and thus, no conclusion could be reached; (2) trust, quantitative analysis partially supported the hypothesis that an active collaborative strategy will promote teamwork attitudes, where the human is open to the robot’s suggestions and to act as a teammate; and (3) regulating expectation, quantitative analysis confirmed that a collaborative strategy promoted a discovery process that regulates the subject’s expectation toward the robot. Overall, we can conclude that an active collaborative strategy impacts favorably the process of realizing empathy compared to a passive one. The results are compelling to move the design of this experiment forward into more comprehensive studies, ultimately leading to a path where we can clearly study engagements that reduce abandonment and disillusionment with the process of realizing empathy as the core design for active collaborative strategies.

  • An automated system recommending background music to listen to while working


    Many people listen to music while working nowadays. However, conventional recommendation systems that are designed for playing songs matching user preferences cannot be applied for such a situation. This is because previous research showed that listeners’ concentration can be negatively affected not only by music that listeners strongly dislike but also by music that the listeners strongly like. Therefore, when we consider a recommendation system to be used while working, it is desirable to avoid both songs the user likes very much and songs the user dislikes very much. Given this background, we propose FocusMusicRecommender, a system designed specifically for recommending music to listen to while working. It summarizes songs automatically and plays them successively in order to enable users to give not only “dislike (very much)” feedback via a “skip” button but also “like (very much)” feedback via a “keep listening” button. The feedback is then combined with the users’ concentration level that is estimated from their behavioral history during the playback of the corresponding song, which allows the system to obtain preference information that distinguishes between “like” and “like very much” without burdening the user who is working. Based on the preference information, the system estimates the preference levels of unplayed songs and prioritizes the songs for subsequent playback by also considering the user’s current concentration level. Our experiments showed the validity and effectiveness of the proposed method, including the accuracy of the concentration level estimation. Moreover, our user study verified the suitability of the recommendation results from both the observed behavior and obtained comments of the participants.

  • Measuring and modeling context-dependent preferences for hearing aid settings


    Despite having individual perceptual preferences toward sounds, hearing aid users often end up with default hearing aid settings that have no contextual awareness. However, the introduction of smartphone-connected hearing aids has enabled a rethinking of hearing aids as user-adaptive systems considering both individual and contextual differences. In this study, we aimed to investigate the feasibility of such context-aware system for providing hearing aid users with a number of relevant hearing aid settings to choose from. During normal real-world hearing aid usage, we applied a smartphone-based method for capturing participants’ listening experience and audiological preference for different intervention levels of three audiological parameters (Noise Reduction, Brightness, Soft Gain). Concurrently, we collected contextual data as both self-reports (listening environment and listening intention) and continuous data logging of the acoustic environment (sound pressure level, signal-to-noise ratio). First, we found that having access to different intervention levels of the Brightness and Soft Gain parameters affected listening satisfaction. Second, for all three audiological parameters, the perceived usefulness of having access to different intervention levels was significantly modulated by context. Third, contextual data improved the prediction of both explicit and implicit intervention level preferences. Our findings highlight that context has a significant impact on hearing aid preferences across participants and that contextual data logging can help reduce the space of potential interventions in a user-adaptive system so that the most useful and preferred settings can be offered. Moreover, the proposed mixed-effects model is suitable for capturing predictions on an individual level and could also be expanded to predictions on a group level by including relevant user features.

  • A human-centered decentralized architecture and recommendation engine in SIoT


    The Internet of Things (IoT) enables smart objects to connect and share information, thus unlocking the potential for end users to receive more and better information and services. In the Social IoT (SIoT), objects adopt a social behavior, where they establish social connections to other objects and can operate autonomously in order to accomplish a given task. In this work, we present an SIoT architecture, called DANOS, based on three principles, dynamicity, decentralization, and anthropomorphism. Specifically, in DANOS (a) smart objects dynamically adapt their social neighborhood depending on the task, (b) information is decentralized and kept private, while efficient discovery mechanisms are prescribed, and (c) smart objects adopt a human-centered behavior determined by the personality traits of their users. We consider a general class of tasks that can be formulated as recommendations, and demonstrate how DANOS orchestrates the objects’ social behavior. An extensive experimental evaluation validates our design choices, showing that three principles together result in improving the effectiveness of recommendations. The key lesson learned from our work is that SIoT architectures can benefit from the adoption of aspects of dynamicity, decentralization, and anthropomorphism.

  • An adaptive decision-making system supported on user preference predictions for human–robot interactive communication


    Adapting to dynamic environments is essential for artificial agents, especially those aiming to communicate with people interactively. In this context, a social robot that adapts its behaviour to different users and proactively suggests their favourite activities may produce a more successful interaction. In this work, we describe how the autonomous decision-making system embedded in our social robot Mini can produce a personalised interactive communication experience by considering the preferences of the user the robot interacts with. We compared the performance of Top Label as Class and Ranking by Pairwise Comparison, two promising algorithms in the area, to find the one that best predicts the user preferences. Although both algorithms provide robust results in preference prediction, we decided to integrate Ranking by Pairwise Comparison since it provides better estimations. The method proposed in this contribution allows the autonomous decision-making system of the robot to work on different modes, balancing activity exploration with the selection of the favourite entertaining activities. The operation of the preference learning system is shown in three real case studies where the decision-making system works differently depending on the user the robot is facing. Then, we conducted a human–robot interaction experiment to investigate whether the robot users perceive the personalised selection of activities more appropriate than selecting the activities at random. The results show how the study participants found the personalised activity selection more appropriate, improving their likeability towards the robot and how intelligent they perceive the system. query Please check the edit made in the article title.