Group Recommender Systems
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Group Recommender Systems
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Author : Alexander Felfernig
language : en
Publisher: Springer
Release Date : 2018-03-07
Group Recommender Systems written by Alexander Felfernig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-07 with Technology & Engineering categories.
This book presents group recommender systems, which focus on the determination of recommendations for groups of users. The authors summarize different technologies and applications of group recommender systems. They include an in-depth discussion of state-of-the-art algorithms, an overview of industrial applications, an inclusion of the aspects of decision biases in groups, and corresponding de-biasing approaches. The book includes a discussion of basic group recommendation methods, aspects of human decision making in groups, and related applications. A discussion of open research issues is included to inspire new related research. The book serves as a reference for researchers and practitioners working on group recommendation related topics.
Group Recommender Systems
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Author : Alexander Felfernig
language : en
Publisher: Springer Nature
Release Date : 2023-11-27
Group Recommender Systems written by Alexander Felfernig and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-27 with Technology & Engineering categories.
This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.
Group Recommender Systems
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Author : Alexander Felfernig
language : en
Publisher:
Release Date : 2023
Group Recommender Systems written by Alexander Felfernig and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Group Recommender Systems
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Author : Alexander Felfernig
language : en
Publisher: Springer
Release Date : 2023-11-29
Group Recommender Systems written by Alexander Felfernig and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-29 with Technology & Engineering categories.
This book discusses different aspects of group recommender systems, which are systems that help to identify recommendations for groups instead of single users. In this context, the authors present different related techniques and applications. The book includes in-depth summaries of group recommendation algorithms, related industrial applications, different aspects of preference construction and explanations, user interface aspects of group recommender systems, and related psychological aspects that play a crucial role in group decision scenarios.
The Adaptive Web
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Author : Peter Brusilovski
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-04-24
The Adaptive Web written by Peter Brusilovski and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-04-24 with Computers categories.
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.
User Interaction In Group Recommender Systems
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Author : Jordi Pascual Milego
language : en
Publisher:
Release Date : 2014
User Interaction In Group Recommender Systems written by Jordi Pascual Milego and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
The project goal is the study and development of specific techniques for obtaining user preferences from several interaction mechanisms with the objective of making recommendations in a conversational group recommender system. In this project, we capture and model user's preferences.
Recommender Systems Handbook
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Author : Francesco Ricci
language : en
Publisher: Springer Nature
Release Date : 2022-04-21
Recommender Systems Handbook written by Francesco Ricci and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-21 with Computers categories.
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.
Ecscw 2001
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Author : Wolfgang Prinz
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Ecscw 2001 written by Wolfgang Prinz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Computers categories.
Schmidt and Bannon (1992) introduced the concept of common information space by contrasting it with technical conceptions of shared information: Cooperative work is not facilitated simply by the provisioning of a shared database, but rather requires the active construction by the participants of a common information space where the meanings of the shared objects are debated and resolved, at least locally and temporarily. (Schmidt and Bannon, p. 22) A CIS, then, encompasses not only the information but also the practices by which actors establish its meaning for their collective work. These negotiated understandings of the information are as important as the availability of the information itself: The actors must attempt to jointly construct a common information space which goes beyond their individual personal information spaces. . . . The common information space is negotiated and established by the actors involved. (Schmidt and Bannon, p. 28) This is not to suggest that actors’ understandings of the information are identical; they are simply “common” enough to coordinate the work. People understand how the information is relevant for their own work. Therefore, individuals engaged in different activities will have different perspectives on the same information. The work of maintaining the common information space is the work that it takes to balance and accommodate these different perspectives. A “bug” report in software development is a simple example. Software developers and quality assurance personnel have access to the same bug report information. However, access to information is not sufficient to coordinate their work.
Recommender Systems Advanced Developments
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Author : Jie Lu
language : en
Publisher: World Scientific
Release Date : 2020-08-04
Recommender Systems Advanced Developments written by Jie Lu and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-04 with Computers categories.
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.
Group Representation Learning For Group Recommendation
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Author : Sarina Sajadi Ghaemmaghami
language : en
Publisher:
Release Date : 2021
Group Representation Learning For Group Recommendation written by Sarina Sajadi Ghaemmaghami and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Group recommender systems facilitate group decision making for a set of individuals (e.g., a group of friends, a team, a corporation, etc.). Existing group recommendation methods mostly learn group members' individual preferences and then aggregate them into a group preference. This thesis takes a different approach. We focus on making recommendations for a new group of users whose preferences are unknown, but we are given the decisions/choices of other groups. By formulating this problem as group recommendation from group implicit feedback, we focus on two of its practical instances: Given a set of groups and their observed decisions, group decision prediction intends to predict the decision of a new group of users whereas reverse social choice aims to infer the preferences of those users involved in observed group decisions. These two problems are of interest to not only group recommendation, but also to personal privacy when the users intend to conceal their personal preferences, but have participated in group decisions. To tackle these two problems, we propose and study DeepGroup - a deep learning approach for group recommendation with group implicit data. We empirically assess the predictive power of DeepGroup on various real-world datasets, group conditions (e.g., homophily or heterophily), and group decision (or voting) rules. Our extensive experiments not only demonstrate the efficacy of DeepGroup but also shed light on the privacy-leakage concerns of some decision making processes.