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Social network-based recommender systems

Schall, Daniel.

Social network-based recommender systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Social network-based recommender systemsby Daniel Schall.
Author:	Schall, Daniel.
Published:	Cham :Springer International Publishing :2015.
Description:	xiii, 126 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Recommender systems (Information filtering)
Online resource:	http://dx.doi.org/10.1007/978-3-319-22735-1
ISBN:	9783319227351$q(electronic bk.)

Social network-based recommender systems
Schall, Daniel.

Social network-based recommender systems[electronic resource] /by Daniel Schall. - Cham :Springer International Publishing :2015. - xiii, 126 p. :ill. (some col.), digital ;24 cm.

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

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.

ISBN: 9783319227351$q(electronic bk.)

Standard No.: 10.1007/978-3-319-22735-1doiSubjects--Topical Terms:

310886
Recommender systems (Information filtering)

LC Class. No.: QA76.9.I58

Dewey Class. No.: 005.56

Social network-based recommender systems
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245 1 0 $a Social network-based recommender systems $h [electronic resource] / $c by Daniel Schall.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xiii, 126 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Overview of Social Recommender Systems -- Link Prediction for Directed Graphs -- Follow Recommendation in Communities -- Partner Recommendation -- Social Broker Recommendation -- Conclusion.
520 $a 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.
650 0 $a Recommender systems (Information filtering) $3 310886
650 0 $a Online social networks. $3 281852
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Information Systems Applications (incl. Internet) $3 530743
650 2 4 $a Graph Theory. $3 522732
650 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 274376
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