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

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  • Exploring the added effect of three recommender system techniques in mobile health interventions for physical activity: a longitudinal randomized controlled trial


    Physical inactivity is a public health issue. Mobile health interventions to promote physical activity often still experience dropout, resulting in people not adhering to the interventions. This paper aims to further improve mobile health apps with innovatively applied techniques from recommender system algorithms to increase personalization for physical activities and practical tips to reduce sedentary behavior. Personalization in our mobile health recommender is achieved with a seven-step algorithm: filtering on user profile (1), current weather and daylight (2), pre-filtering with a micro-profile on current mood and motivation (3), content-based recommendations using our own two datasets extended with 24 attributes (4), post-filtering on estimated current situation (5), adapting and gradually increasing duration and intensity (6), and generating just-in-time adaptive interventions (7). To analyze the effectiveness of steps 3, 4, and 5, a double-blind randomized controlled trial is conducted in which only the experimental group receives the three additional personalization steps, while the control group replaces these steps with a random selection. As such, the control group’s recommendations are still partly personalized with the other steps. Participants install the app on their Android smartphone and use the app for eight weeks, with a pretest and posttest questionnaire, and a follow-up after six months. The experimental group assigned significantly higher star ratings to the recommendations, and significantly higher momentary motivation for physical activities, tips, and manual user refreshes, compared to the control group. Additionally, there was less dropout and a significantly stronger increase in duration and intensity of the performed physical activities in the experimental group. Because the experimental group received the three additional personalization steps with micro-profiling, content-based recommender, and post-filtering on estimated situation, our results suggest that these three steps resulted in more personalized recommendations that motivate users more. Future research should aim to further improve personalization to increase the effectiveness of mobile health interventions and effectively motivate people to move more.

  • A survey on popularity bias in recommender systems


    Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey, therefore, includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.

  • Example, nudge, or practice? Assessing metacognitive knowledge transfer of factual and procedural learners


    Factual knowledge and procedural knowledge are knowing ‘That’ and ‘How,’ respectively, whereas conditional knowledge is the metacognitive knowledge of ‘When’ and ‘Why.’ As prior work has found that students with conditional knowledge spontaneously transferred such knowledge across intelligent tutoring systems, this work assesses the impact of metacognitive interventions on the knowledge transfer of factual and procedural students. Specifically, we used a between-subject, pre-/posttest design with factual and procedural students, each randomly assigned to either the example, nudge, practice, or control condition. The interventions taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Meanwhile, conditional students received no interventions. Six weeks later, we trained all students on a probability tutor that only supports BC without interventions. Our results suggest that nudges for factual students and practice for their procedural peers are the key factors for catching up with conditional students on both tutors and for facilitating knowledge transfer from the logic to probability tutor. We discuss two potential complementary theories for our findings: a choice-based theory (from interventions to knowledge) and a metacognitive load-based theory (from knowledge to interventions). The choice-based theory maps the amount of choice in the interventions to knowledge types, while the metacognitive load-based theory associates knowledge types with the metacognitive load each intervention offers. Implications for practice are discussed.

  • The impacts of relevance of recommendations and goal commitment on user experience in news recommender design


    Cold start and data sparsity are problems hindering the function of news recommender systems. Optimally serving first-time users through relevant news article recommendations is an application of these problems that have attracted scholars’ attention. Users’ goal commitment might be another solution that raise efficiency of information searching while it is understudied in previous research. Drawing from the results of 669 Amazon MTurk workers’ questionnaires, this experimental study explored solutions. We manipulated the relevance of news recommendations (high relevance vs. low relevance) and information behavior within a news portal, either scanning (via a list of news articles) or seeking (via a search query). We also measured an individual difference variable, goal commitment. Results indicated that higher relevance of recommendations and higher goal commitment lead to lower information overload, higher user satisfaction, and lower information anxiety. We also found interaction effects of goal commitment and content relevance on article selection, such that users will be likely to select more irrelevant articles in the low relevance condition rather than the high relevance condition even though they have a goal commitment and perceive higher information overload and information anxiety indirectly via selecting more irrelevant articles. Furthermore, people with high goal commitment were less anxious when they read fewer irrelevant articles in the news recommender systems. The study addressed the importance of considering the user-recommender interaction and the potential merits of considering users goal commitment in the news recommender system design. The research indicates integrating personal traits into state-of-the-art news recommender systems has the potential to significantly improve user experience. While this research suggests personal traits can mitigate the limitations of imperfect recommender systems, users can also curate or train these systems based on their goals to further enhance efficiency.

  • Generalisable sensor-free frustration detection in online learning environments using machine learning


    Learning can generally be categorised into three domains, which include cognitive (thinking), affective (emotions or feeling) and psychomotor (physical or kinesthetic). In the learner model, acknowledging the affective aspects of learning is important for a range of learner outcomes, including motivation, persistence, and engagement. Learners’ affective states can be detected using physical (e.g. cameras) and physiological sensors (e.g., EEG) in online learning. Although these detectors demonstrate high accuracy, they raise privacy concerns for learners and present challenges in deploying them on a large scale to larger groups of students or in classroom settings. Consequently, researchers have designed an alternative method that can recognise students’ affective states at any point during online learning from their interaction with a computer-based learning platform (i.e. intelligent tutoring systems) without using any sensors. Existing sensor-free affect detectors however, are less accurate and not directly generalisable to other domains and systems. This research focuses on developing generalisable sensor-free affect detectors to identify students’ frustration during online learning using machine learning classifiers. The detectors were built by identifying minimal optimal features associated with frustration from the high-dimensional feature space through a series of experiments on a real-world students’ affective dataset, which are generalisable across various learning platforms and domains. To evaluate their accuracy and generalisability, the detectors’ performance was validated on two independent datasets collected from different educational institutions. The experimental results show that cost-sensitive Bayesian classifiers can achieve higher affect detection accuracies with a small number of generalisable features compared to other classifiers.