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

19 September 2019

The latest content available from Springer
  • Learning to recommend similar items from human judgments


    Similar item recommendations—a common feature of many Web sites—point users to other interesting objects given a currently inspected item. A common way of computing such recommendations is to use a similarity function, which expresses how much alike two given objects are. Such similarity functions are usually designed based on the specifics of the given application domain. In this work, we explore how such functions can be learned from human judgments of similarities between objects, using two domains of “quality and taste”—cooking recipe and movie recommendation—as guiding scenarios. In our approach, we first collect a few thousand pairwise similarity assessments with the help of crowdworkers. Using these data, we then train different machine learning models that can be used as similarity functions to compare objects. Offline analyses reveal for both application domains that models that combine different types of item characteristics are the best predictors for human-perceived similarity. To further validate the usefulness of the learned models, we conducted additional user studies. In these studies, we exposed participants to similar item recommendations using a set of models that were trained with different feature subsets. The results showed that the combined models that exhibited the best offline prediction performance led to the highest user-perceived similarity, but also to recommendations that were considered useful by the participants, thus confirming the feasibility of our approach.

  • Gaze analysis of user characteristics in magazine style narrative visualizations


    Previous research has shown that various user characteristics (e.g., cognitive abilities, personality traits, and learning abilities) can influence user experience during information visualization tasks. These findings have prompted researchers to investigate user-adaptive information visualizations that can help users by providing personalized support based on their specific needs. Whereas existing work has been mostly limited to tasks involving just visualizations, the aim of our research is to broaden this work to include scenarios where users process textual documents with embedded visualizations, i.e., Magazine Style Narrative Visualizations, or MSNVs for short. In this paper, we analyze eye tracking data collected from a user study with MSNVs to uncover processing behaviors that are negatively impacting user experience (i.e., time on task) for users with low abilities in these user characteristics. Our analysis leverages Linear Mixed-Effects Models to evaluate the relationships among user characteristics, gaze processing behaviors, and task performance. Our results identify several MSNV processing behaviors within the visualization that contribute to poor task performance for users with low reading proficiency. For instance, we identify that users with low reading proficiency transition significantly more often compared to their counterparts between relevant and non-relevant bars, and transition more often from bars to the labels. We present our findings as a step toward designing user-adaptive support mechanisms to alleviate these difficulties with MSNVs, and provide suggestions on how our results can be leveraged for creating a set of meaningful interventions for future evaluation (e.g., dynamically highlighting relevant bars and labels in the visualization to help users with low reading proficiency locate them more effectively).

  • Preface to the special issue on learning analytics and personalised support across spaces
  • Personalized weight loss strategies by mining activity tracker data


    Wearable devices make self-monitoring easier by the users, who usually tend to increase physical activity and weight loss maintenance over time. But in terms of behavior adaptation to these goals, these devices do not provide specific features beyond monitoring the achievement of daily goals, such as a number of steps or miles walked and caloric outtake. The purpose of this study is twofold. By analyzing a large dataset of signals collected by these devices, we identify significant clusters of similar behavior patterns related to user physical activities. We then examine specific patterns of step count in the context of recommendation of habits that more likely give rise to weight loss effects. The evaluation of the effectiveness of these personalized recommendations, based on a comparative study, proves how a recommender system based on the reinforcement learning paradigm is able to guarantee better performance for this task by balancing the trade-off between long-term and short-term rewards.

  • Equipping the ACT-R cognitive architecture with a temporal ratio model of memory and using it in a new intelligent adaptive interface


    ACT-R, as a useful and well-known cognitive architecture, is a theory for simulating and understanding human cognition. However, the standard version of this architecture uses a deprecated forgetting model. So, we equipped it with a temporal ratio model of memory that has been named as SIMPLE (Scale-Independent Memory, Perception, and Learning). On the other hand, one of the usages of cognitive architectures is to model the user in an Intelligent Adaptive Interface (IAI) implementation. Thus, our motivation for this effort is to use this equipped ACT-R in an IAI to deliver the right information at the right time to users based on their cognitive needs. So, to test our proposed equipped ACT-R, we designed and implemented a new IAI to control a swarm of Unmanned Aerial Vehicles (UAVs). This IAI uses the equipped ACT-R for user cognitive modeling, to deliver the right information to the users based on their forgetting model. Thus, our contributions are: equipping the ACT-R cognitive architecture with the SIMPLE memory model and using this equipped version of ACT-R for user modeling in a new IAI to control a group of UAVs. Simulation results, which have been obtained using different subjective and objective measures, show that we significantly improved situation awareness of the users using the IAI empowered by our equipped ACT-R.