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

19 July 2019

The latest content available from Springer
  • Exploring user behavioral data for adaptive cybersecurity


    This paper describes an exploratory investigation into the feasibility of predictive analytics of user behavioral data as a possible aid in developing effective user models for adaptive cybersecurity. Partial least squares structural equation modeling is applied to the domain of cybersecurity by collecting data on users’ attitude towards digital security, and analyzing how that influences their adoption and usage of technological security controls. Bayesian-network modeling is then applied to integrate the behavioral variables with simulated sensory data and/or logs from a web browsing session and other empirical data gathered to support personalized adaptive cybersecurity decision-making. Results from the empirical study show that predictive analytics is feasible in the context of behavioral cybersecurity, and can aid in the generation of useful heuristics for the design and development of adaptive cybersecurity mechanisms. Predictive analytics can also aid in encoding digital security behavioral knowledge that can support the adaptation and/or automation of operations in the domain of cybersecurity. The experimental results demonstrate the effectiveness of the techniques applied to extract input data for the Bayesian-based models for personalized adaptive cybersecurity assistance.

  • Gameful Experience Questionnaire (GAMEFULQUEST): an instrument for measuring the perceived gamefulness of system use


    In this paper, we present the development and validation of an instrument for measuring users’ gameful experience while using a service. Either intentionally or unintentionally, systems and services are becoming increasingly gamified and having a gameful experience is progressively important for the user’s overall experience of a service. Gamification refers to the transformation of technology to become more game-like, with the intention of evoking similar positive experiences and motivations that games do (the gameful experience) and affecting user behavior. In this study, we used a mixed-methods approach to develop an instrument for measuring the gameful experience. In a first qualitative study, we developed a model of the gameful experience using data from a questionnaire consisting of open-ended questions posed to users of Zombies, Run!, Duolingo, and Nike+ Run Club. In a second study, we developed the instrument and evaluated its dimensionality and psychometric properties using data from users of Zombies, Run! (N = 371). Based on the results of this second study, we further developed the instrument in a third study using data from users of Duolingo (N = 507), in which we repeated the assessment of dimensionality and psychometric properties, this time including confirmation of the model. As a result of this work, we devised GAMEFULQUEST, an instrument that can be used to model and measure an individual user’s gameful experience in systems and services, which can be used for user-adapted gamification and for informing user-modeling research within a gamification context.

  • Subprofile-aware diversification of recommendations


    A user of a recommender system is more likely to be satisfied by one or more of the recommendations if each individual recommendation is relevant to her but additionally if the set of recommendations is diverse. The most common approach to recommendation diversification uses re-ranking: the recommender system scores a set of candidate items for relevance to the user; it then re-ranks the candidates so that the subset that it will recommend achieves a balance between relevance and diversity. Ordinarily, we expect a trade-off between relevance and diversity: the diversity of the set of recommendations increases by including items that have lower relevance scores but which are different from the items already in the set. In early work, the diversity of a set of recommendations was given by the average of their distances from one another, according to some semantic distance metric defined on item features such as movie genres. More recent intent-aware diversification methods formulate diversity in terms of coverage and relevance of aspects. The aspects are most commonly defined in terms of item features. By trying to ensure that the aspects of a set of recommended items cover the aspects of the items in the user’s profile, the level of diversity is more personalized. In offline experiments on pre-collected datasets, intent-aware diversification using item features as aspects sometimes defies the relevance/diversity trade-off: there are configurations in which the recommendations exhibits increases in both relevance and diversity. In this paper, we present a new form of intent-aware diversification, which we call SPAD (Subprofile-Aware Diversification), and a variant called RSPAD (Relevance-based SPAD). In SPAD, the aspects are not item features; they are subprofiles of the user’s profile. We present and compare a number of different ways to extract subprofiles from a user’s profile. None of them is defined in terms of item features. Therefore, SPAD is useful even in domains where item features are not available or are of low quality. On three pre-collected datasets from three different domains (movies, music artists and books), we compare SPAD and RSPAD to intent-aware methods in which aspects are item features. We find on these datasets that SPAD and RSPAD suffer even less from the relevance/diversity trade-off: across all three datasets, they increase both relevance and diversity for even more configurations than other approaches to diversification. Moreover, we find that SPAD and RSPAD are the most accurate systems across all three datasets.

  • A methodology for creating and validating psychological stories for conveying and measuring psychological traits


    Personality impacts all areas of our lives; it governs who we are and how we react to life’s challenges. Personalized systems that adapt to end users should take into account the user’s personality to perform well. Several methodologies (e.g. User-as-Wizard, indirect studies) that use personality adaptation require first for personality to be conveyed to the participant; this has few validated approaches. Furthermore, measuring personality is often time consuming, prone to response bias (e.g. using questionnaires) or data intensive (e.g. using behaviour or text mining). This paper presents a methodology for creating and validating stories to convey psychological traits and for using such stories with a personality slider scale to measure these traits. We present the validation of the scale and evaluate its reliability. To evidence the validity of the methodology, we outline studies where the stories and scale have been effectively applied (in recommender systems, intelligent tutoring systems, and persuasive systems).

  • Personalized support for well-being at work: an overview of the SWELL project


    Recent advances in wearable sensor technology and smartphones enable simple and affordable collection of personal analytics. This paper reflects on the lessons learned in the SWELL project that addressed the design of user-centered ICT applications for self-management of vitality in the domain of knowledge workers. These workers often have a sedentary lifestyle and are susceptible to mental health effects due to a high workload. We present the sense–reason–act framework that is the basis of the SWELL approach and we provide an overview of the individual studies carried out in SWELL. In this paper, we revisit our work on reasoning: interpreting raw heterogeneous sensor data, and acting: providing personalized feedback to support behavioural change. We conclude that simple affordable sensors can be used to classify user behaviour and heath status in a physically non-intrusive way. The interpreted data can be used to inform personalized feedback strategies. Further longitudinal studies can now be initiated to assess the effectiveness of m-Health interventions using the SWELL methods.