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


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19 April 2024

The latest content available from Springer
  • Exploring raw data transformations on inertial sensor data to model user expertise when learning psychomotor skills

    Abstract

    This paper introduces a novel approach for leveraging inertial data to discern expertise levels in motor skill execution, specifically distinguishing between experts and beginners. By implementing inertial data transformation and fusion techniques, we conduct a comprehensive analysis of motor behaviour. Our approach goes beyond conventional assessments, providing nuanced insights into the underlying patterns of movement. Additionally, we explore the potential for utilising this data-driven methodology to aid novice practitioners in enhancing their performance. The findings showcase the efficacy of this approach in accurately identifying proficiency levels and lay the groundwork for personalised interventions to support skill refinement and mastery. This research contributes to the field of motor skill assessment and intervention strategies, with broad implications for sports training, physical rehabilitation, and performance optimisation across various domains.

  • Personalized recommendations for learning activities in online environments: a modular rule-based approach

    Abstract

    Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.

  • Modeling of anticipation using instance-based learning: application to automation surprise in aviation using passive BCI and eye-tracking data

    Abstract

    Human-centered artificial intelligence (HCAI) needs to be able to adapt to anticipated user behavior. We argue that the anticipation capabilities required for HCAI adaptation can be modeled best with the help of a cognitive architecture. This paper introduces an ACT-R cognitive model that uses instance-based learning to observe and learn situations and actions in the form of mental models. These mental models enable the anticipation of the behavior of individual users. The model is applied to a use case of automation surprise in commercial aviation to test how anticipation can best be modeled for cockpit applications. Empirical data from a flight simulator study including behavioral, neurophysiological and eye-tracking measures from 13 pilots were used to evaluate the model. Results show that the accuracy of the model is significantly higher than chance, demonstrating that combining context information, user state data and a cognitive model can enable HCAI adaptation based on anticipated user behavior.

  • Federated privacy-preserving collaborative filtering for on-device next app prediction

    Abstract

    In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices, and the transmission of users’ data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of our approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model, we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage.

  • Personalization of industrial human–robot communication through domain adaptation based on user feedback

    Abstract

    Achieving safe collaboration between humans and robots in an industrial work-cell requires effective communication. This can be achieved through a robot perception system developed using data-driven machine learning. The challenge for human–robot communication is the availability of extensive, labelled datasets for training. Due to the variations in human behaviour and the impact of environmental conditions on the performance of perception models, models trained on standard, publicly available datasets fail to generalize well to domain and application-specific scenarios. Thus, model personalization involving the adaptation of such models to the individual humans involved in the task in the given environment would lead to better model performance. A novel framework is presented that leverages robust modes of communication and gathers feedback from the human partner to auto-label the mode with the sparse dataset. The strength of the contribution lies in using in-commensurable multimodes of inputs for personalizing models with user-specific data. The personalization through feedback-enabled human–robot communication (PF-HRCom) framework is implemented on the use of facial expression recognition as a safety feature to ensure that the human partner is engaged in the collaborative task with the robot. Additionally, PF-HRCom has been applied to a real-time human–robot handover task with a robotic manipulator. The perception module of the manipulator adapts to the user’s facial expressions and personalizes the model using feedback. Having said that, the framework is applicable to other combinations of multimodal inputs in human–robot collaboration applications.