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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.
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.
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.
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.
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.
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 book presents the concept of a fuzzy-based recommender system for user account privacy settings that can be used for citizen participation on online political platforms. The elaborated components are exemplarily based on the needs of a political platform implemented during the presidential election in Ecuador. The book readdresses the issue of privacy paradox demonstrating that, indeed, users’ actual decisions of being private in most cases diverge with their initial privacy intentions. The two concepts presented in the book - the citizen privacy profile framework and the prototype fuzzy-based privacy settings recommender system - can be adapted by different organizations such as government institutions, NGOs, or private online service providers to meet their specific needs. The book will be of interest to researchers and practitioners in the areas of usage modeling, privacy, system design, and for service providers in eDemocracy.
This book constitutes the refereed proceedings of the 13th International Conference on Persuasive Technology, PERSUASIVE 2018, held in Waterloo, ON, Canada, in April 2018. The 21 revised full papers and 4 short papers presented were carefully reviewed and selected from 59 submissions. The papers demonstrate how persuasive technologies can help solve societal issues. They explore new frontiers for persuasive technology, such as personalized persuasion, new sensor usage, uses of big data, and new ways of creating engagement through gaming or social connection, focusing on a variety of technologies (e.g., web, wearables, AI, and smart environments). The papers are organized in the following topical sections: social means to persuasion; nudging and just-in-time interventions; design principles and practices; persuasive games; personalization and tailoring; and theoretical reflections.
This book constitutes the refereed proceedings of the 9th International Conference on Persuasive Technology, PERSUASIVE 2014, held in Padua, Italy, in May 2014. The 27 revised full papers and 12 revised short papers presented were carefully reviewed and selected from 58 submissions. In addition to the themes of persuasive technology dealt with in the previous editions of the conference, this edition highlighted a special theme, i.e. persuasive, motivating, empowering videogames.
Acclaimed by various content platforms (books, music, movies) and auction sites online, recommendation systems are key elements of digital strategies. If development was originally intended for the performance of information systems, the issues are now massively moved on logical optimization of the customer relationship, with the main objective to maximize potential sales. On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.