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).

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Latest Results for User Modeling and User-Adapted Interaction

26 September 2022

The latest content available from Springer
  • Conceptualization and development of an autonomous and personalized early literacy content and robot tutor behavior for preschool children


    Personalized learning has a higher impact on students’ progress than traditional approaches. However, current resources required to implement personalization are scarce. This research aims to conceptualize and develop an autonomous robot tutor with personalization policy for preschool children aged between three to five years old. Personalization is performed by automatically adjusting the difficulty level of the lesson delivery and assessment, as well as adjusting the feedback based on the reaction of children. This study explores three child behaviors for the personalization policy: (i) academic knowledge (measured by the correctness of the answer), (ii) executive functioning of attention (measured by the orientation and the gaze direction of child’s body), and (iii) working memory or hesitation (measured by the time lag before the answer). Moreover, this study designed lesson content through interviews with teachers and deployed the personalization interaction policy through the NAO robot with five children in a case user study method. We qualitatively analyze the session observations and parent interviews, as well as quantitatively analyze knowledge gain through pre- and posttests and a parent questionnaire. The findings of the study reveal that the personalized interaction with the robot showed a positive potential in increasing the children’s learning gains and attracting their engagement. As general guidelines based on this pilot study, we identified additional personalization strategies that could be used for autonomous personalization policies based on each child’s behavior, which could have a considerable impact on child learning.

  • Preface to the special issue on dynamic recommender systems and user models
  • Safe, effective and explainable drug recommendation based on medical data integration


    Medicine recommendation denotes the task of predicting drug combinations for patients’ therapies in case of complex diseases such as cancer or diabetes. These patients often follow a treatment consisting of multiple drugs simultaneously. Previous research works have already made predictions of next drug combinations based on the integration of the patients’ health records with an adverse drug–drug interaction (DDI) knowledge graph in order to minimize drug side effects. However, they missed to consider additional valuable information coming from synergistic drug–drug interaction knowledge graphs. In this paper, we integrate Electronic Health Record graph data incorporating patient, disease, therapy and drug information with either a synergistic or an adverse DDI knowledge graph to recommend safe and explainable medications. To the best of our knowledge, we are the first ones to compare three different medical data integration strategies based on the analysis stage (i.e. early, intermediate and late) at which integration takes place. By identifying those drugs that either complement each other or behave adversely, we are able to improve the efficacy of the patient’s therapy and/or minimize the toxicity and side effects. Moreover, we develop models to support doctors in comprehensively screening candidate drugs and their possible substitutes by providing also robust explanations alongside with the recommended medicine. We run experiments with three real-life medical data sets. In terms of suggesting safe and effective drugs, our proposed method suggests 34 times more synergistic drugs than the baseline algorithm for the cancer data set and reduces the unwanted side effects to patients’ medication by 2350 times more than the baseline algorithm for the MIMIC III data set. Our results demonstrate that we can assist doctors to prescribe effective, safe and explainable medication for the patients’ treatment.

  • Automatically detecting task-unrelated thoughts during conversations using keystroke analysis


    Task-unrelated thought (TUT), commonly referred to as mind wandering, is a mental state where a person’s attention moves away from the task-at-hand. This state is extremely common, yet not much is known about how to measure it, especially during dyadic interactions. We thus built a model to detect when a person experiences TUTs while talking to another person through a computer-mediated conversation, using their keystroke patterns. The best model was able to differentiate between task-unrelated thoughts and task-related thoughts with a kappa of 0.363, using features extracted from a 15 second window. We also present a feature analysis to provide additional insights into how various typing behaviors can be linked to our ongoing mental states.

  • Fair performance-based user recommendation in eCoaching systems


    Offering timely support to users in eCoaching systems is a key factor to keep them engaged. However, coaches usually follow a lot of users, so it is hard for them to prioritize those with whom they should interact first. Timeliness is especially needed when health implications might be the consequence of a lack of support. In this paper, we focus on this last scenario, by considering an eCoaching platform for runners. Our goal is to provide a coach with a ranked list of users, according to the support they need. Moreover, we want to guarantee a fair exposure in the ranking, to make sure that users of different groups have equal opportunities to get supported. In order to do so, we first model their performance and running behavior and then present a ranking algorithm to recommend users to coaches, according to their performance in the last running session and the quality of the previous ones. We provide measures of fairness that allow us to assess the exposure of users of different groups in the ranking and propose a re-ranking algorithm to guarantee a fair exposure. Experiments on data coming from the previously mentioned platform for runners show the effectiveness of our approach on standard metrics for ranking quality assessment and its capability to provide a fair exposure to users. The source code and the preprocessed datasets are available at: