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Social Network-Based Recommender Systems

Autor Daniel Schall
en Limba Engleză Hardback – oct 2015
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 alsofind this books useful as a secondary text.
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Specificații

ISBN-13: 9783319227344
ISBN-10: 3319227343
Pagini: 126
Ilustrații: XIII, 126 p.
Dimensiuni: 155 x 235 x 17 mm
Greutate: 0.38 kg
Ediția:1st ed. 2015
Editura: Springer International Publishing
Colecția Springer
Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

Overview of Social Recommender Systems.- Link Prediction for Directed Graphs.- Follow Recommendation in Communities.- Partner Recommendation.- Social Broker Recommendation.- Conclusion.

Recenzii

“The book is quite brief. It contains a lot of rather technical information concentrated around particular topics. … I highly recommend this book to students, professionals, experts, and others interested in the potential of recommendations taking place within social networks.” (P. Navrat, Computing Reviews, computingreviews.com, June, 2016)

Caracteristici

Introduces novel concepts and techniques about the formation of social networks and each chapter concludes with an analysis and summary Provides real world datasets from GitHub, Facebook, Twitter, Google Plus, and the European Union ICT research collaborations Presents a range of mathematical models, ranking algorithms, software frameworks and datasets