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Recommender Systems and the Social Web
There is an increasing demand for recommender systems due to the information overload users are facing on the Web. The goal of a recommender system is to provide personalized recommendations of products or services to users. With the advent of the Social Web, user-generated content has enriched the social dimension of the Web. As user-provided content data also tells us something about the user, one can learn the user’s individual preferences from the Social Web. This opens up completely new opportunities and challenges for recommender systems research. Fatih Gedikli deals with the question of how user-provided tagging data can be used to build better recommender systems. A tag recommen...
Recommender Systems
In this age of information overload, people use a variety of strategies to make choices about what to buy, how to spend their leisure time, and even whom to date. Recommender systems automate some of these strategies with the goal of providing affordable, personal, and high-quality recommendations. This book offers an overview of approaches to developing state-of-the-art recommender systems. The authors present current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches. They also discuss how to measure the effectiveness of recommender systems and illustrate the methods with practical case studies. The final chapters cover emerging topics such as recommender systems in the social web and consumer buying behavior theory. Suitable for computer science researchers and students interested in getting an overview of the field, this book will also be useful for professionals looking for the right technology to build real-world recommender systems.
Personalized Human-Computer Interaction
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.
Graph-Powered Machine Learning
1. Machine learning and graphs : an introduction -- 2. Graph data engineering -- 3. Graphs in machine learning applications -- 4. Content-based recommendations -- 5. Collaborative filtering -- 6. Session-based recommendations -- 7. Context-aware and hybrid recommendations -- 8. Basic approaches to graph-powered fraud detection -- 9. Proximity-based algorithms -- 10. Social metwork analysis against fraud -- 11. Graph-based natural language processing -- 12. Knowledge graphs.
Research and Development in Intelligent Systems XIX
The papers in this volume are the refereed technical papers presented at ES 2002, the Twenty-second SGAI International Conference on Knowledge Based Systems and Applied Artificial Intelligence, held in Cambridge in December 2002. The papers in this volume present new and innovative developments in the field, divided into sections on Machine Learning, Knowledge Representation and Reasoning, Knowledge Acquisition, Constraint Satisfaction and Scheduling, and Natural Language Processing. This is the nineteenth volume in the Research and Development series. The series is essential reading for those who wish to keep up to date with developments in this important field. The Application Stream papers are published as a companion volume under the title Applications and Innovations in Intelligent Systems X.
Knowledge Transformation for the Semantic Web
This guide covers main issues in transforming the vast majority of models to be used in the context of the semantic web: XML schemas, relational models, UML diagrams, RDF schemas and ontologies. Different practical approaches are presented as well as discussions on some theoretical issues.
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Web Engineering
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