Marko Tkalčič – författare

This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.

This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users.

This book investigates the potential of combining the more quantitative - data-driven techniques with the more qualitative - theory-driven approaches towards the design of user-centred intelligent systems. It seeks to explore the potential of incorporating factors grounded in psychological theory into adaptive/intelligent routines, mechanisms, technologies and innovations. It highlights models, methods and tools that are emerging from their convergence along with challenges and lessons learned. Special emphasis is placed on promoting original insights and paradigms with respect to latest technologies, current research trends, and innovation directions, e.g., incorporating variables derived from psychological theory and individual differences in adaptive intelligent systems so as to increase explainability, fairness, and transparency, and decrease bias during interactions while the control remains with the user.

This book investigates the potential of combining the more quantitative - data-driven techniques with the more qualitative - theory-driven approaches towards the design of user-centred intelligent systems. It seeks to explore the potential of incorporating factors grounded in psychological theory into adaptive/intelligent routines, mechanisms, technologies and innovations. It highlights models, methods and tools that are emerging from their convergence along with challenges and lessons learned. Special emphasis is placed on promoting original insights and paradigms with respect to latest technologies, current research trends, and innovation directions, e.g., incorporating variables derived from psychological theory and individual differences in adaptive intelligent systems so as to increase explainability, fairness, and transparency, and decrease bias during interactions while the control remains with the user.

Personalization is ubiquitous from search engines to online-shopping websites helping us find content more efficiently and this book focuses on the key developments that are shaping our daily online experiences. With advances in the detection of end users’ emotions, personality, sentiment and social signals, researchers and practitioners now have the tools to build a new generation of personalized systems that will really understand the user’s state and deliver the right content. With leading experts from a vast array of domains from user modeling, mobile sensing and information retrieval to artificial intelligence, human-computer interaction (HCI) social computing and psychology, a broad spectrum of topics are covered.  From discussing psychological theoretical models and exploring state-of-the-art methods for acquiring emotions and personality in an unobtrusive way, as well as describing how these concepts can be used to improve various aspects of the personalization processand chapters that discuss evaluation and privacy issues.Emotions and Personality in Personalized Systems will help aid researchers and practitioners develop and evaluate user-centric personalization systems that take into account the factors that have a tremendous impact on our decision-making – emotions and personality.

This book presents group recommender systems, which focus on the determination of recommendations for groups of users. The authors summarize different technologies and applications of group recommender systems. They include an in-depth discussion of state-of-the-art algorithms, an overview of industrial applications, an inclusion of the aspects of decision biases in groups, and corresponding de-biasing approaches. The book includes a discussion of basic group recommendation methods, aspects of human decision making in groups, and related applications. A discussion of open research issues is included to inspire new related research. The book serves as a reference for researchers and practitioners working on group recommendation related topics.