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

01 December 2022

The latest content available from Springer
  • How do item features and user characteristics affect users’ perceptions of recommendation serendipity? A cross-domain analysis

    Abstract

    Serendipity is one of beyond-accuracy objectives for recommender systems (RSs), which aims to achieve both relevance and unexpectedness of recommendations, so as to potentially address the “filter bubble” issue of traditional accuracy-oriented RSs. However, so far most of the serendipity-oriented studies have focused on developing algorithms to consider various types of item features or user characteristics, but are largely based on their own assumptions. Few have stood from users’ perspective to identify the effects of these features on users’ perceptions of the serendipity of the recommendation. Therefore, in this paper, we have analyzed their effects with two user survey datasets. These are the Movielens Serendipity Dataset of 467 users’ responses to a retrospective survey of their perceptions of the recommended movie’s serendipity, and the Taobao Serendipity Dataset of 11,383 users’ perceptions of the serendipity of a recommendation received at a mobile e-commerce platform. In both datasets, we have analyzed the correlations between users’ serendipity perceptions and various types of item features (i.e., item-driven such as popularity, profile-driven such as in-profile diversity, and interaction-driven including category-level and item-level features), as well as the influence of several user characteristics (including the Big-Five personality traits and curiosity). The results disclose both domain-independent and domain-specific observations, which may be constructive in enhancing current serendipity-oriented recommender systems by better utilizing item features and user data.

  • Gaze-based predictive models of deep reading comprehension

    Abstract

    Eye gaze patterns can reveal user attention, reading fluency, corrective responding, and other reading processes, suggesting they can be used to develop automated, real-time assessments of comprehension. However, past work has focused on modeling factual comprehension, whereas we ask whether gaze patterns reflect deeper levels of comprehension where inferencing and elaboration are key. We trained linear regression and random forest models to predict the quality of users’ open-ended self-explanations (SEs) collected both during and after reading and scored on a continuous scale by human raters. Our models use theoretically grounded eye tracking features (number and duration of fixations, saccade distance, proportion of regressive and horizontal saccades, spatial dispersion of fixations, and reading time) captured from a remote, head-free eye tracker (Tobii TX300) as adult users read a long expository text (6500 words) in two studies (N = 106 and 131; 247 total). Our models: (1) demonstrated convergence with human-scored SEs (r = .322 and .354), by capturing both within-user and between-user differences in comprehension; (2) were distinct from alternate models of mind-wandering and shallow comprehension; (3) predicted multiple-choice posttests of inference-level comprehension (r = .288, .354) measured immediately after reading and after a week-long delay beyond the comparison models; and (4) generalized across new users and datasets. Such models could be embedded in digital reading interfaces to improve comprehension outcomes by delivering interventions based on users’ level of comprehension.

  • A dichotomic approach to adaptive interaction for socially assistive robots

    Abstract

    Socially assistive robotics (SAR) aims at designing robots capable of guaranteeing social interaction to human users in a variety of assistance scenarios that range, e.g., from giving reminders for medications to monitoring of Activity of Daily Living, from giving advices to promote an healthy lifestyle to psychological monitoring. Among possible users, frail older adults deserve a special focus as they present a rich variability in terms of both alternative possible assistive scenarios (e.g., hospital or domestic environments) and caring needs that could change over time according to their health conditions. In this perspective, robot behaviors should be customized according to properly designed user models. One of the long-term research goals for SAR is the realization of robots capable of, on the one hand, personalizing assistance according to different health-related conditions/states of users and, on the other, adapting behaviors according to heterogeneous contexts as well as changing/evolving needs of users. This work proposes a solution based on a user model grounded on the international classification of functioning, disability and health (ICF) and a novel control architecture inspired by the dual-process theory. The proposed approach is general and can be deployed in many different scenarios. In this paper, we focus on a social robot in charge of the synthesis of personalized training sessions for the cognitive stimulation of older adults, customizing the adaptive verbal behavior according to the characteristics of the users and to their dynamic reactions when interacting. Evaluations with a restricted number of users show good usability of the system, a general positive attitude of users and the ability of the system to capture users personality so as to adapt the content accordingly during the verbal interaction.

  • Research directions in recommender systems for health and well-being

    Abstract

    Recommender systems have been put to use in the entertainment and e-commerce domains for decades, and in these decades, recommender systems have grown and matured into reliable and ubiquitous systems in today’s digital landscape. Relying on this maturity, the application of recommender systems for health and well-being has seen a rise in recent years, paving the way for tailored and personalized systems that support caretakers, caregivers, and other users in the health domain. In this introduction, we give a brief overview of the stakes, the requirements, and the possibilities that recommender systems for health and well-being bring.

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

    Abstract

    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.