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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. 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.
This book highlights cutting-edge research in the field of network science, offering scientists, researchers and graduate students a unique opportunity to catch up on the latest advances in theory and a multitude of applications. It presents the peer-reviewed proceedings of the fifth International Workshop on Complex Networks & their Applications (COMPLEX NETWORKS 2016), which took place in Milan during the last week of November 2016. The carefully selected papers are divided into 11 sections reflecting the diversity and richness of research areas in the field. More specifically, the following topics are covered: Network models; Network measures; Community structure; Network dynamics; Diffusion, epidemics and spreading processes; Resilience and control; Network visualization; Social and political networks; Networks in finance and economics; Biological and ecological networks; and Network analysis.
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.
Frontiers in Big Data is delighted to present the ‘Reviews in Recommender Systems’ series of article collections. Reviews in Recommender Systems will publish high-quality scholarly review papers on key topics in recommender systems and their applications in our everyday lives, in search engines, online retail, news, entertainment, travel, social networks, and much more. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. We anticipate the research presented will promote discussion in the Big Data community that will translate to best practice applications in further research, industry, real-world implementations, public health, and policy settings.
This book presents advances in speech and music in the domain of audio signal processing. The book begins with introductory chapters on the basics of speech and music, and then proceeds to computational aspects of speech and music, including music information retrieval and spoken language processing. The authors discuss the intersection in the field of computer science, musicology and speech analysis, and how the multifaceted nature of speech and music information processing requires unique algorithms, systems using sophisticated signal processing, and machine learning techniques that better extract useful information. The authors discuss how a deep understanding of both speech and music in ...
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A detailed analysis of existing credibility models from different information seeking research areas, with a focus on the Web and its pervasive social component. Defines the limits of credibility with respect to digital information access systems, providing the reader with an organized and comprehensive reference guide to the state of the art.