Personalized systems are systems that adapt themselves to meet the inferred needs of individual users. The majority of personalized systems mainly rely on data describing how users interacted with these systems. A common approach is to use historical data to predict users’ future needs, preferences, and behavior to subsequently adapt the system to these predictions. However, this adaptation is often done without leveraging the theoretical understanding between behavior and user traits that can be used to characterize individual users or the relationship between user traits and needs that can be used to adapt the system. Adopting a more theoretical perspective can benefit personalization in two ways: (i) letting systems rely on theory can reduce the need for extensive data-driven analysis, and (ii) interpreting the outcomes of datadriven analysis (such as predictive models) from a theoretical perspective can expand our knowledge about users. However, incorporating theoretical knowledge in personalization brings forth a number of challenges. In this chapter, we review literature that taps into aspects of (i) psychological models from traditional psychological theory that can be used in personalization, (ii) relationships between psychological models and online behavior, (iii) automated inference of psychological models from data, and (iv) how to incorporate psychological models in personalized systems. Finally, we propose a step-by-step approach on how to design personalized systems that take user traits into account.
Computer applications, and especially the Internet, provide many people with disabilities with unique opportunities for interpersonal communication, social interaction, and active participation (including access to labor and entertainment). Nevertheless, rigid user interfaces often present accessibility barriers to people with physical, sensory, or cognitive impairments. Accordingly, user interface personalization is crucial to overcome these barriers, allowing a considerable section of the population with disabilities to have computer access. Adapting the user interface to people with disabilities requires taking into consideration their physical, sensory, or cognitive abilities and restrictions and then providing alternative access procedures according to their capacities. This chapter presents methods and techniques that are applied to research and practice on user interface personalization for people with disabilities and discusses possible approaches for diverse application fields where personalization is required: accessibility to the web using transcoding, web mining for eGovernment, and human-robot interaction for people with severe motor restrictions.
Workplace learning has been part of our everyday reality since a long time, but at present, it has become more important than ever before. New technological opportunities can radically change not only formal, but also informal (unintentional) learning, typical for the workplace. Nowadays companies face a new challenge, which is the transition towards Industry 4.0. It is a complex process that concerns both executives and employees. Therefore, it is important to find solutions that make it easier for both sides. This change is accompanied by numerous re-qualification requirements, which demand a radical improvement of workplace learning and on-the-job training. Recent developments enable a more precise understanding of users’ needs, which can lead to better personalization of learning experiences. The effectiveness and efficiency of training and work processes can be improved through wearable technologies and augmented reality. Information technology should support the whole spectrum of educational methodologies, including personalized guidance, collaborative learning, and training of practical skills, as well as meta-cognitive scaffolding. Here we provide a reflective view on the former progress of adaptive workplace learning assistance (especially in the European context) and then point out several prospective approaches that aim to address the current issues. These should lead to innovative context-sensitive and intelligent adaptive assistance systems that support learning and training at the workplace.
This chapter discusses main opportunities and challenges of assessing and utilizing personality traits in personalized interactive systems and services. This unique perspective arises from our long-term collaboration on research projects involving three groups on human-computer interaction (HCI), psychology, and statistics. Currently, personalization in HCI is often based on past user behavior, preferences, and interaction context. We argue that personality traits provide a promising additional source of information for personalization, which goes beyond context- and device-specific behavior and preferences. We first give an overview of the well-established Big Five personality trait model from psychology. We then present previous findings on the influence of personality in HCI associated with the benefits and challenges of personalization. These findings include the preference for interactive systems, filtering of information to increase personal relevance, communication behavior, and the impact on trust and acceptance. Moreover, we present first approaches of personality-based recommender systems. We then identify several opportunities and use cases for personality-aware personalization: (i) personal communication between users, (ii) recommendations upon first use, (iii) persuasive technology, (iv) trust and comfort in autonomous vehicles, and (v) empathic intelligent systems. Furthermore, we highlight main challenges. First, we point out technological challenges of personality computing. To benefit from personality awareness, systems need to automatically assess the user’s personality. To create empathic intelligent agents (e. g., voice assistants), a consistent personality has to be synthesized. Second, personality-aware personalization raises questions about user concerns and views, particularly privacy and data control. Another challenge is acceptance and trust in personality-aware systems due to the sensitivity of the data. Moreover, the importance of an accurate mental model for users’ trust in a system was recently underlined by the right for explanations in the EU’s General Data Protection Regulation. Such considerations seem particularly relevant for systems that assess and utilize personality. Finally, we examine methodological requirements such as the need for large sample sizes and appropriate measurements. We conclude with a summary of opportunities and challenges of personality-aware personalization and discuss future research questions.
Personalization, aiming at supporting users individually and according to their individual needs and prerequisites, has been discussed in a number of domains, including learning, searching, or information retrieval. In the field of human- computer interaction, personalization also bears high potential as users might exhibit varying and strongly individual preferences and abilities related to interaction. For instance, users with certain kinds of motor impairments might not be able to use certain input devices and methods, such as touchscreens and touch-based interaction. At least a high amount of time consuming individual configuration typically arises. Further, interaction preferences might also vary among people without known impairments. Thus, personalized interaction, taking into account these prerequisites, might offer individualized support and solutions to potential problems. Personalized interaction involves automated selection and configuration of input devices but also adaptation of applications and user interfaces. This chapter discusses personalized interaction generally and presents a software framework that provides a template for a feasible technical infrastructure. Further, it explains a specific case study of personalized interaction that was implemented on the basis of the framework, and discusses an evaluation process and results for this use case.
A closer alignment of mutual expectations between technical systems and their users regarding functionality and interactions is supposed to improve their overall performance. In general, such an alignment is realized by automatically adapting the appearance and the behavior of a system. Adaptation may be based on parameters regarding the task to be fulfilled, the surrounding context, or the user himself. Among the latter, current emphasis of research is shifting from a user’s trails in the system (for instance, to derive his level of expertise) towards transient aspects (like his current mental or emotional state). For educational technology, in particular, adapting the presented information and the tasks to be solved to the current personal needs of a learner promises a higher motivation and thus a better learning outcome. Tasks which are equally challenging and motivating can keep the users in a state of flow and thus foster enduring engagement. This is of certain importance for difficult topics and/or learners with disabilities. The chapter explains the complex cause-and-effect models behind adaptive training systems, the mechanisms that can be facilitated to implement them, and empirical results from a clinical study. We exemplify this for the training of emotion recognition by people with autism, but not limited to this user group. For this purpose, we present two approaches. One is to extent the Elo algorithm regarding dimensions of difficulty in social cognition. This allows not only to judge the difficulty of tasks and the skills of users, but also to freely generate well-suited tasks. The second approach is to make use of socio-emotional signals of the learners in order to further adapt the training system. We discuss current possibilities and remaining challenges for these approaches.
Adaptive, personalized recommendations have become a common feature of today’s web and mobile app user interfaces. In most of modern applications, however, the underlying recommender systems are black boxes for the users, and no detailed information is provided about why certain items were selected for recommendation. Users also often have very limited means to influence (e. g., correct) the provided suggestions and to apply information filters. This can potentially lead to a limited acceptance of the recommendation system. In this chapter, we review explanations and feedback mechanisms as a means of building trustworthy recommender and advice giving systems that put their users in control of the personalization process, and we outline existing challenges in the area.