Название: Personalized Human-Computer Interaction, 2nd editionАвтор: Mirjam Augstein, Eelco Herder, Wolfgang Worndl
Издательство: De Gruyter
Год: 2023
Страниц: 376
Язык: английский
Формат: pdf (true), epub
Размер: 42.0 MB
Personalized and adaptive systems employ user models to adapt content, services, interaction or navigation to individual users’ needs. User models can be inferred from implicitly observed information, such as the user’s interaction history or current location, or from explicitly entered information, such as user profile data or ratings. Applications of personalization include item recommendation, location-based services, learning assistance and the tailored selection of interaction modalities.
With the transition from desktop computers to mobile devices and ubiquitous environments, the need for adapting to changing contexts is even more important. However, this also poses new challenges concerning privacy issues, user control, transparency, and explainability. In addition, user experience and other human factors are becoming increasingly important.
Recommender systems aim at facilitating users’ search and decision-making when they are faced with a large number of available options, such as buying products online or selecting music tracks to listen to. A broad range of Machine Learning models and algorithms has been developed that aim at predicting users’ assessment of unseen items and at recommending items that best match their interests. However, it has been shown that optimizing the system in terms of algorithm accuracy often does not result in a correspondingly high level of user satisfaction. Therefore, a more user-centric approach to developing recommender systems is needed that better takes into account users’ actual goals, the current context and their cognitive demands.
Recommender systems (RS) aim at supporting users in their search and decision-making process when interacting with online systems in a variety of application domains, such as e-commerce, media streaming or social media. Due to the very large number of items typically available on such platforms, RS have become widespread, almost indispensable tools for counteracting the choice overload problem that often results from such large sets of options. In contrast to other types of AI-based systems, RS address the task of preferential choice where there is not a single correct outcome but a ranking of options that may match the user’s preferences to different degrees and that is, therefore, mostly personalized. The development of RS has in the past been largely driven from an algorithmic perspective, focusing on algorithm effectiveness and accuracy, defined as correctly predicting the user’s likely choices based on signals collected from past user interactions with the system.
This book describes foundations of user modeling, discusses user interaction as a basis for adaptivity, and showcases several personalization approaches in a variety of domains, including music recommendation, tourism, and accessible user interfaces.
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