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

More books from Springer International Publishing

Cover of the book Algorithms for Sensor Systems by Daniel Schall
Cover of the book Computer Network Security by Daniel Schall
Cover of the book Pathology of the Maxillofacial Bones by Daniel Schall
Cover of the book Policy Practice and Digital Science by Daniel Schall
Cover of the book Evaluation of Supply Chain Performance by Daniel Schall
Cover of the book Tympanic Membrane Retraction Pocket by Daniel Schall
Cover of the book Language Learning and Use in English-Medium Higher Education by Daniel Schall
Cover of the book Universities in the Age of Reform, 1800–1870 by Daniel Schall
Cover of the book Mobile Electric Vehicles by Daniel Schall
Cover of the book Computer Games by Daniel Schall
Cover of the book Paleobiodiversity and Tectono-Sedimentary Records in the Mediterranean Tethys and Related Eastern Areas by Daniel Schall
Cover of the book LIFE - AS A MATTER OF FAT by Daniel Schall
Cover of the book Immunogenetics of Fungal Diseases by Daniel Schall
Cover of the book Urban Utopias by Daniel Schall
Cover of the book Green Adsorbents for Pollutant Removal by Daniel Schall
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy