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This state-of-the-art survey provides a systematic overview of the ideas and techniques of the adaptive Web and serves as a central source of information for researchers, practitioners, and students. The volume constitutes a comprehensive and carefully planned collection of chapters that map out the most important areas of the adaptive Web, each solicited from the experts and leaders in the field.
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.
This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.
Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.
Realizing the growing importance of semantic adaptation and personalization of media, the editors of this book brought together leading researchers and practitioners of the field to discuss the state-of-the-art, and explore emerging exciting developments. This volume comprises extended versions of selected papers presented at the 1st International Workshop on Semantic Media Adaptation and Personalization (SMAP 2006), which took place in Athens in December 2006.
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
How do you design personalized user experiences that delight and provide value to the customers of an eCommerce site? Personalization does not guarantee high quality user experience: a personalized user experience has the best chance of success if it is developed using a set of best practices in HCI. In this book 35 experts from academia, industry and government focus on issues in the design of personalized web sites. The topics range from the design and evaluation of user interfaces and tools to information architecture and computer programming related to commercial web sites. The book covers four main areas: -Theoretical, Conceptual, and Architectural Frameworks of Personalization, -Research on the Design and Evaluation of Personalized User Experiences in Different Domains, -Approaches to personalization Through Recommender Systems, -Lessons Learned and Future Research Questions. This book will be a valuable tool in helping the reader to understand the range of factors to take into consideration in designing and building a personalized user experience. The authors of each of the chapters identify possibilities and alert the reader to issues that can be addressed in the beginning of a project by taking a 'big picture' view of designing personalized user interfaces. For anyone working or studying in the field of HCI, information architecture or eCommerce, this book will provide a solid foundation of knowledge and prepare for the challenges ahead.
Global information retrieval and anywhere, anytime information access has stimulated a need to design and model the personalized information search in a flexible and agile way that can use the specific personalization techniques, algorithms, and available technology infrastructure to satisfy high-level functional requirements for personalization. Personalized Information Retrieval and Access: Concepts, Methods and Practices surveys the main concepts, methods, and practices of personalized information retrieval and access in today's data intensive, dynamic, and distributed environment, and provides students, researchers, and practitioners with authoritative coverage of recent technological advances that are shaping the future of globally distributed information retrieval and anywhere, anytime information access.
This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.