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Author: Kyung-Hyan Yoo
Publisher: Springer Science & Business Media
ISBN: 146144702X
Category : Computers
Languages : en
Pages : 62
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Book Description
Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding influential factors in advice seeking relationships, which is abundant in the context of human-human relationships, can provide an important framework for identifying potential influence factors in recommender system context. This book reviews the existing literature on the factors in advice seeking relationships in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.
Author: Kyung-Hyan Yoo
Publisher: Springer Science & Business Media
ISBN: 146144702X
Category : Computers
Languages : en
Pages : 62
Get Book Here
Book Description
Whether users are likely to accept the recommendations provided by a recommender system is of utmost importance to system designers and the marketers who implement them. By conceptualizing the advice seeking and giving relationship as a fundamentally social process, important avenues for understanding the persuasiveness of recommender systems open up. Specifically, research regarding influential factors in advice seeking relationships, which is abundant in the context of human-human relationships, can provide an important framework for identifying potential influence factors in recommender system context. This book reviews the existing literature on the factors in advice seeking relationships in the context of human-human, human-computer, and human-recommender system interactions. It concludes that many social cues that have been identified as influential in other contexts have yet to be implemented and tested with respect to recommender systems. Implications for recommender system research and design are discussed.
Author: Yvonne de Kort
Publisher: Springer
ISBN: 3540770062
Category : Computers
Languages : en
Pages : 328
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Book Description
This book constitutes the thoroughly refereed post-proceedings of the Second International Conference on Persuasive Technology for Human Well-Being, PERSUASIVE 2007, held in Palo Alto, CA, USA, in April 2007. The 37 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers are organized in topical sections and cover a broad range of subjects.
Author: Francesco Ricci
Publisher: Springer
ISBN: 148997637X
Category : Computers
Languages : en
Pages : 1008
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Book Description
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.
Author: Francesco Ricci
Publisher: Springer Science & Business Media
ISBN: 0387858202
Category : Computers
Languages : en
Pages : 848
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Book Description
The explosive growth of e-commerce and online environments has made the issue of information search and selection increasingly serious; users are overloaded by options to consider and they may not have the time or knowledge to personally evaluate these options. Recommender systems have proven to be a valuable way for online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. Correspondingly, various techniques for recommendation generation have been proposed. During the last decade, many of them have also been successfully deployed in commercial environments. Recommender Systems Handbook, an edited volume, is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. Theoreticians and practitioners from these fields continually seek techniques for more efficient, cost-effective and accurate recommender systems. This handbook aims to impose a degree of order on this diversity, by presenting a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, challenges and applications. Extensive artificial applications, a variety of real-world applications, and detailed case studies are included. Recommender Systems Handbook illustrates how this technology can support the user in decision-making, planning and purchasing processes. It works for well known corporations such as Amazon, Google, Microsoft and AT&T. This handbook is suitable for researchers and advanced-level students in computer science as a reference.
Author: Nilufar Baghaei
Publisher: Springer Nature
ISBN: 3030984389
Category : Computers
Languages : en
Pages : 287
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Book Description
This book constitutes the refereed post-conference proceedings of the 17th International Conference on Persuasive Technology, PERSUASIVE 2022, held as a virtual event, in March 2022. The 13 full papers presented in this book together with 7 short papers were carefully reviewed and selected from 46 submissions.
Author: Peter W. de Vries
Publisher: Springer
ISBN: 3319551345
Category : Computers
Languages : en
Pages : 309
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Book Description
This book constitutes the refereed proceedings of the 12th International Conference on Persuasive Technology, PERSUASIVE 2017, held in Amsterdam, The Netherlands, in April 2017. The 23 revised full papers presented were carefully reviewed and selected from 85 submissions. The papers are grouped in topical sections on health(care), monitoring, and coaching; personality, personalization, and persuasion; motivations, facilitators, and barriers; design principles and strategies.
Author: Dietmar Jannach
Publisher: Cambridge University Press
ISBN: 1139492594
Category : Computers
Languages : en
Pages :
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Book Description
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.
Author: Sharda, Nalin
Publisher: IGI Global
ISBN: 1605668192
Category : Computers
Languages : en
Pages : 354
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Book Description
"This book presents innovative research being conducted into Travel Recommender Systems, travel related on-line communities, and their user interface design"--Provided by publisher.
Author: Sandra Burri Gram-Hansen
Publisher: Springer Nature
ISBN: 3030457125
Category : Computers
Languages : en
Pages : 252
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Book Description
This book constitutes the refereed proceedings of the 15th International Conference on Persuasive Technology, PERSUASIVE 2020, held in Aalborg, Denmark, in April 2020. The 18 full papers presented in this book were carefully reviewed and selected from 79 submissions. The papers are grouped in the following topical sections: methodological and theoretical perspectives on persuasive design; persuasive in practice, digital insights; persuasive technologies for health and wellbeing; persuasive solutions for a sustainable future; and on security and ethics in persuasive technology.
Author: P. Pavan Kumar
Publisher: CRC Press
ISBN: 1000387275
Category : Computers
Languages : en
Pages : 249
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Book Description
Recommender systems use information filtering to predict user preferences. They are becoming a vital part of e-business and are used in a wide variety of industries, ranging from entertainment and social networking to information technology, tourism, education, agriculture, healthcare, manufacturing, and retail. Recommender Systems: Algorithms and Applications dives into the theoretical underpinnings of these systems and looks at how this theory is applied and implemented in actual systems. The book examines several classes of recommendation algorithms, including Machine learning algorithms Community detection algorithms Filtering algorithms Various efficient and robust product recommender systems using machine learning algorithms are helpful in filtering and exploring unseen data by users for better prediction and extrapolation of decisions. These are providing a wider range of solutions to such challenges as imbalanced data set problems, cold-start problems, and long tail problems. This book also looks at fundamental ontological positions that form the foundations of recommender systems and explain why certain recommendations are predicted over others. Techniques and approaches for developing recommender systems are also investigated. These can help with implementing algorithms as systems and include A latent-factor technique for model-based filtering systems Collaborative filtering approaches Content-based approaches Finally, this book examines actual systems for social networking, recommending consumer products, and predicting risk in software engineering projects.
