Download or read book Persuasive Recommender Systems written by Kyung-Hyan Yoo and published by Springer Science & Business Media. This book was released on 2012-08-17 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Recommender Systems as Persuasion Technology written by Melinda M. McGucken and published by . This book was released on 2015 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the influence of the six principles of persuasion on five types of recommender system to determine the mechanism of persuasion influence for each type of recommender in e-commerce applications. We undertook an interdisicplinary literature review including references from persuasion in social psychology, recommender systems in artificial intelligence, and persuasion technology in psychology and computer science was conducted and applicable resources analyzed. Our table was compiled based on supporting literature and analysis to examine the intersection between persuasion principle, type of recommender, and whether that recommender leverages the principle and the method of influence utilized by each in e-commerce applications. We conclude that robust support has been found in the literature supporting recommender systems as a persuasion technology that is inherently persuasive capabilities. Research ans analysis support recommender systems as an effective tool to drive sales and boost revenues in e-commerce. Ethical implications, as well as future research directions, are explored and complete this present inquiry.

Download or read book Recommender Systems written by Gérald Kembellec and published by John Wiley & Sons. This book was released on 2014-12-04 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Industrial Recommender System written by Lantao Hu and published by Springer Nature. This book was released on with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Recommender Systems Advanced Developments written by Jie Lu and published by World Scientific. This book was released on 2020-08-04 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recommender systems provide users (businesses or individuals) with personalized online recommendations of products or information, to address the problem of information overload and improve personalized services. Recent successful applications of recommender systems are providing solutions to transform online services for e-government, e-business, e-commerce, e-shopping, e-library, e-learning, e-tourism, and more.This unique compendium not only describes theoretical research but also reports on new application developments, prototypes, and real-world case studies of recommender systems. The comprehensive volume provides readers with a timely snapshot of how new recommendation methods and algorithms can overcome challenging issues. Furthermore, the monograph systematically presents three dimensions of recommender systems — basic recommender system concepts, advanced recommender system methods, and real-world recommender system applications.By providing state-of-the-art knowledge, this excellent reference text will immensely benefit researchers, managers, and professionals in business, government, and education to understand the concepts, methods, algorithms and application developments in recommender systems.

Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Persuasive Technology written by Yvonne de Kort and published by Springer. This book was released on 2007-11-24 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer Nature. This book was released on 2022-04-21 with total page 1053 pages. Available in PDF, EPUB and Kindle. Book excerpt: This third edition handbook describes in detail the classical methods as well as extensions and novel approaches that were more recently introduced within this field. It consists of five parts: general recommendation techniques, special recommendation techniques, value and impact of recommender systems, human computer interaction, and applications. The first part presents the most popular and fundamental techniques currently used for building recommender systems, such as collaborative filtering, semantic-based methods, recommender systems based on implicit feedback, neural networks and context-aware methods. The second part of this handbook introduces more advanced recommendation techniques, such as session-based recommender systems, adversarial machine learning for recommender systems, group recommendation techniques, reciprocal recommenders systems, natural language techniques for recommender systems and cross-domain approaches to recommender systems. The third part covers a wide perspective to the evaluation of recommender systems with papers on methods for evaluating recommender systems, their value and impact, the multi-stakeholder perspective of recommender systems, the analysis of the fairness, novelty and diversity in recommender systems. The fourth part contains a few chapters on the human computer dimension of recommender systems, with research on the role of explanation, the user personality and how to effectively support individual and group decision with recommender systems. The last part focusses on application in several important areas, such as, food, music, fashion and multimedia recommendation. This informative third edition handbook provides a comprehensive, yet concise and convenient reference source to recommender systems for researchers and advanced-level students focused on computer science and data science. Professionals working in data analytics that are using recommendation and personalization techniques will also find this handbook a useful tool.

Download or read book Recommender System with Machine Learning and Artificial Intelligence written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2020-07-08 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book 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. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.

Download or read book Empirical Findings on Persuasiveness of Recommender Systems for Customer Decision Support in Electronic Commerce written by Qinyu Liao and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness.

Download or read book Personalization Techniques and Recommender Systems written by Gulden Uchyigit and published by World Scientific. This book was released on 2008 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: The phenomenal growth of the Internet has resulted in huge amounts of online information, a situation that is overwhelming to the end users. To overcome this problem, personalization technologies have been extensively employed.The book is the first of its kind, representing research efforts in the diversity of personalization and recommendation techniques. These include user modeling, content, collaborative, hybrid and knowledge-based recommender systems. It presents theoretic research in the context of various applications from mobile information access, marketing and sales and web services, to library and personalized TV recommendation systems.This volume will serve as a basis to researchers who wish to learn more in the field of recommender systems, and also to those intending to deploy advanced personalization techniques in their systems.

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer Science & Business Media. This book was released on 2010-10-21 with total page 848 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Beyond Recommendation Accuracy written by Ala'a Nasir Al-slaity and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the emergence of Recommender Systems (RS), most of the research has focused on improving the capability of a recommender system to predict and provide an accurate recommendation. However, the literature has demonstrated increasing evidence that providing accurate recommendations is not sufficient to increase users' acceptance of the provided recommendations. Hence, it is vital for a recommender system to focus not only on the accuracy of the provided recommendations but also on other factors that influence the acceptance of recommendations and the extent to which these recommendations are convincing or persuasive. Consequently, there becomes a need for new research paradigms to help improve the capabilities of recommender systems, which goes beyond the recommendation accuracy. One of the recently emerged research directions that consider this need fosters the idea of adopting human-related theories from the social sciences domain, such as persuasiveness of social communication. In this context, however, a challenging, non-trivial, and not fully explored issue that arises is: how to integrate human-related theories into a recommender system to be one of its intrinsic characteristics in order to improve its performance beyond its accuracy? This thesis aims to address the above issue from two angles: first, it investigates improving recommender systems by increasing users' acceptance of the recommendations. To achieve this, the influence of persuasion principles on users of recommender systems is investigated. Then a reference architecture framework to adapt and integrate persuasion features as a substantial characteristic of recommender systems is proposed. The proposed framework, named Personalized Persuasive RS (PerPer), adopts concepts from the social sciences literature, namely personality traits and persuasion principles. In addition, PerPer adapts machine learning concepts, in particular, the Learning Automata, to support its learning capabilities. Second, the thesis discusses evaluating recommender systems beyond their accuracy. Particularly, it proposes two evaluation approaches that aim to evaluate recommender systems in a comprehensive way that goes beyond evaluating accuracy only. The first evaluation approach is called the Comprehensive Performance evaluation (ComPer). It adopts concepts from the human learning domain and provides a simple, thorough, and setting-independent evaluation approach for recommenders. The essence of ComPer is to consider a recommender system as a human being, and hence the former's outcomes (i.e., recommendations) can be evaluated and validated in a way similar to how humans' learning outcomes are evaluated. The second evaluation approach adopts goal-oriented modeling to provide an evaluation that does not only assess recommenders beyond their accuracy but also considers the multi-stakeholders of RSs. We demonstrate, empirically, and by user studies, the feasibility and usefulness of the proposed approaches. The contributions of the thesis are: (1) A characterization of recommender systems as systems supported with human traits and features, which goes beyond the conventional recommender systems known in the literature. (2) A user study that examines the impact of persuasive principles on users of recommender systems. (3) A Personalized Persuasive RS (PerPer) reference architecture framework to enrich recommender systems with persuasion capabilities that are personalized and adaptive for different users. (4) A mapping between human's cognitive skills and the recommendation process. (5) The Comprehensive Performance evaluation (ComPer) framework to provide a comprehensive assessment of recommender systems considering multiple evaluation dimensions other than accuracy. And (6) a goal-oriented evaluation approach to assess the impact of multiple alternatives for recommendation approaches on the satisfaction of RSs stakeholders' goals.

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Group Recommender Systems written by Alexander Felfernig and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book EMPIRICAL FINDINGS ON PERSUASIVENESS OF RECOMMENDER SYSTEMS FOR CUSTOMER DECISION SUPPORT IN ELECTRONIC COMMERCE written by and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness.

Download or read book Persuasive Technology Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors written by Peter W. de Vries and published by Springer. This book was released on 2017-03-13 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.