Social Network-Based Recommender Systems

Nonfiction, Science & Nature, Mathematics, Graphic Methods, Computers, Advanced Computing, Information Technology, General Computing
Cover of the book Social Network-Based Recommender Systems by Daniel Schall, Springer International Publishing
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Author: Daniel Schall ISBN: 9783319227351
Publisher: Springer International Publishing Publication: September 23, 2015
Imprint: Springer Language: English
Author: Daniel Schall
ISBN: 9783319227351
Publisher: Springer International Publishing
Publication: September 23, 2015
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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