| 1. |
- Graus, Mark, et al.
(författare)
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Theory-grounded user modeling for personalized HCI
- 2019
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Ingår i: Personalized human-computer interaction. - Berlin : Walter de Gruyter. - 9783110552485 - 9783110552614
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Bokkapitel (refereegranskat)abstract
- 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 cater 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 data-driven 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 users’ traits into account.
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| 2. |
- Knees, Peter, et al.
(författare)
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User Awareness in Music Recommender Systems
- 2019
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Ingår i: Personalized human-computer interaction. - Berlin : Walter de Gruyter. - 9783110552485 - 9783110552614
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Bokkapitel (refereegranskat)abstract
- Music recommender systems are a widely adopted application of personalized systems and interfaces.By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (content-based methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods) or a mixture of these with other features (hybrid methods).In this chapter, we focus on the listener's aspects of music recommender systems.We discuss different factors influencing relevance for recommendation on both the listener's and the music's side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i.e., personality traits and demographic characteristics, and cultural features, i.e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior, and (iii) listener intention, in particular by studying music information behavior, i.e., how people seek, find, and use music information.This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.
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| 3. |
- Li, Xiangdong, et al.
(författare)
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Towards personalized virtual reality touring through cross-object user interfaces
- 2019
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Ingår i: Personalized Human-Computer Interaction. - : Walter de Gruyter. - 9783110552485 ; , s. 201-222
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Real-time adaptation is one of the most important problems that currently require a solution in the field of personalized human-computer interaction. For conventional desktop system interactions, user behaviors are acquired to develop models that support context-aware interactions. In virtual reality interactions, however, users operate tools in the physical world but view virtual objects in the virtual world. This dichotomy constrains the use of conventional behavioral models and presents difficulties to personalizing interactions in virtual environments. To address this problem, we propose the cross-object user interfaces (COUIs) for personalized virtual reality touring. COUIs consist of two components: a Deep Learning algorithm-based model using convolutional neural networks (CNNs) to predict the user’s visual attention from the past eye movement patterns to determine which virtual objects are likely to be viewed next, and delivery mechanisms that determine what should when and where be displayed on the user interface. In this chapter, we elaborate on the training and testing of the prediction model and evaluate the delivery mechanisms of COUIs through a cognitive walk-through approach. Furthermore, the implications for using COUIs to personalize interactions in virtual reality (and other environments such as augmented reality and mixed reality) are discussed.
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