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.

Socially enhanced Services Computing deals with a novel and exciting new field at the intersection between Social Computing, Service-oriented Computing, Crowd Computing, and Cloud Computing. The present work presents a collection of selected papers by the editors of this volume, which they feel will help the reader in understanding this field. The approach discussed allows for a seamless integration of people into trusted dynamic compositions of Human-provided Services and Software-based services, thus empowering new interaction models and processes in massive collaboration scenarios in a Future Internet.

At a fundamental level, service-oriented crowdsourcing applies the principles of service-oriented architecture (SOA) to the discovery, composition and selection of a scalable human workforce. Service-Oriented Crowdsourcing: Architecture, Protocols and Algorithms provides both an analysis of contemporary crowdsourcing systems, such as Amazon Mechanical Turk, and a statistical description of task-based marketplaces. The book also introduces a novel mixed service-oriented computing paradigm by providing an architectural description of the Human-Provided Services (HPS) framework and the application of social principles to human coordination and delegation actions. Finally, it examines previously investigated concepts and applies them to business process management integration, including the extension of XML-based industry standards and the instantiation of flexible processes in crowdsourcing environments. Service-Oriented Crowdsourcing is intended for researchers and other academics as an in-depth guide to developing new applications based on crowdsourcing platforms and evaluating various selection and ranking algorithms. Practitioners and other industry professionals will also find this book invaluable.

Socially enhanced Services Computing deals with a novel and exciting new field at the intersection between Social Computing, Service-oriented Computing, Crowd Computing, and Cloud Computing. The present work presents a collection of selected papers by the editors of this volume, which they feel will help the reader in understanding this field. The approach discussed allows for a seamless integration of people into trusted dynamic compositions of Human-provided Services and Software-based services, thus empowering new interaction models and processes in massive collaboration scenarios in a Future Internet.

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.