Trust Networks for Recommender Systems PDF Download

Author: Patricia Victor
Publisher: Springer Science & Business Media
ISBN: 9491216082
Category : Computers
Languages : en
Pages : 210

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Book Description
This book describes research performed in the context of trust/distrust propagation and aggregation, and their use in recommender systems. This is a hot research topic with important implications for various application areas. The main innovative contributions of the work are: -new bilattice-based model for trust and distrust, allowing for ignorance and inconsistency -proposals for various propagation and aggregation operators, including the analysis of mathematical properties -Evaluation of these operators on real data, including a discussion on the data sets and their characteristics. -A novel approach for identifying controversial items in a recommender system -An analysis on the utility of including distrust in recommender systems -Various approaches for trust based recommendations (a.o. base on collaborative filtering), an in depth experimental analysis, and proposal for a hybrid approach -Analysis of various user types in recommender systems to optimize bootstrapping of cold start users.

Author: Jennifer Golbeck
Publisher: Springer Science & Business Media
ISBN: 1848003560
Category : Computers
Languages : en
Pages : 335

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Book Description
This book has evolved out of roughly ve years of working on computing with social trust. In the beginning, getting people to accept that social networks and the relationships in them could be the basis for interesting, relevant, and exciting c- puter science was a struggle. Today, social networking and social computing have become hot topics, and those of us doing research in this space are nally nding a wealth of opportunities to share our work and to collaborate with others. This book is a collection of chapters that cover all the major areas of research in this space. I hope it will serve as a guide to students and researchers who want a strong introduction to work in the eld, and as encouragement and direction for those who are considering bringing their own techniques to bear on some of these problems. It has been an honor and privilege to work with these authors for whom I have so much respect and admiration. Thanks to all of them for their outstanding work, which speaks for itself, and for patiently enduringall my emails. Thanks, as always, to Jim Hendler for his constant support. Cai Ziegler has been particularly helpful, both as a collaborator, and in the early stages of development for this book. My appreciation also goes to Beverley Ford, Rebecca Mowat and everyone at Springer who helped with publication of this work.

Author: Sachi Nandan Mohanty
Publisher: John Wiley & Sons
ISBN: 1119711576
Category : Computers
Languages : en
Pages : 448

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Book Description
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.

Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319296590
Category : Computers
Languages : en
Pages : 518

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Book Description
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific 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. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

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: Xiaofeng Chen
Publisher: Springer Nature
ISBN: 3030624633
Category : Computers
Languages : en
Pages : 554

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Book Description
This three volume book set constitutes the proceedings of the Third International Conference on Machine Learning for Cyber Security, ML4CS 2020, held in Xi’an, China in October 2020. The 118 full papers and 40 short papers presented were carefully reviewed and selected from 360 submissions. The papers offer a wide range of the following subjects: Machine learning, security, privacy-preserving, cyber security, Adversarial machine Learning, Malware detection and analysis, Data mining, and Artificial Intelligence.

Author: Evan Gilman
Publisher: "O'Reilly Media, Inc."
ISBN: 149196216X
Category : Computers
Languages : en
Pages : 240

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Book Description
The perimeter defenses guarding your network perhaps are not as secure as you think. Hosts behind the firewall have no defenses of their own, so when a host in the "trusted" zone is breached, access to your data center is not far behind. That’s an all-too-familiar scenario today. With this practical book, you’ll learn the principles behind zero trust architecture, along with details necessary to implement it. The Zero Trust Model treats all hosts as if they’re internet-facing, and considers the entire network to be compromised and hostile. By taking this approach, you’ll focus on building strong authentication, authorization, and encryption throughout, while providing compartmentalized access and better operational agility. Understand how perimeter-based defenses have evolved to become the broken model we use today Explore two case studies of zero trust in production networks on the client side (Google) and on the server side (PagerDuty) Get example configuration for open source tools that you can use to build a zero trust network Learn how to migrate from a perimeter-based network to a zero trust network in production

Author: Daniel Schall
Publisher: Springer
ISBN: 3319227351
Category : Computers
Languages : en
Pages : 139

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Book Description
This book introduces novel techniques and algorithms necessary to support the formation of social networks. Concepts such as link prediction, graph patterns, recommendation systems based on user reputation, strategic partner selection, collaborative systems and network formation based on ‘social brokers’ are presented. Chapters cover a wide range of models and algorithms, including graph models and a personalized PageRank model. Extensive experiments and scenarios using real world datasets from GitHub, Facebook, Twitter, Google Plus and the European Union ICT research collaborations serve to enhance reader understanding of the material with clear applications. Each chapter concludes with an analysis and detailed summary. Social Network-Based Recommender Systems is designed as a reference for professionals and researchers working in social network analysis and companies working on recommender systems. Advanced-level students studying computer science, statistics or mathematics will also find this books useful as a secondary text.

Author: Osman Khalid
Publisher: Institution of Engineering and Technology
ISBN: 1785619756
Category : Computers
Languages : en
Pages : 369

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Book Description
First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users’ data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.

Author: Wolfgang Lindner
Publisher: Springer Science & Business Media
ISBN: 3540233059
Category : Computers
Languages : en
Pages : 626

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Book Description
This book constitutes the thoroughly refereed joint post-proceedings of five workshops held as part of the 9th International Conference on Extending Database Technology, EDBT 2004, held in Heraklion, Crete, Greece, in March 2004. The 55 revised full papers presented together with 2 invited papers and the summaries of 2 panels were selected from numerous submissions during two rounds of reviewing and revision. In accordance with the topical focus of the respective workshops, the papers are organized in sections on database technology in general (PhD Workshop), database technologies for handling XML information on the Web, pervasive information management, peer-to-peer computing and databases, and clustering information over the Web.