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Social network-based recommender systems [electronic resource]

Social network-based recommender systems [electronic resource]

Material type
E-Book(소장)
Personal Author
Schall, Daniel.
Title Statement
Social network-based recommender systems [electronic resource] / Daniel Schall.
Publication, Distribution, etc
Cham :   Springer International Publishing :   Imprint: Springer,   2015.  
Physical Medium
1 online resource (xiii, 126 p.) : ill. (some col.).
ISBN
9783319227351
요약
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.
General Note
Title from e-Book title page.  
Content Notes
Overview of Social Recommender Systems -- Link Prediction for Directed Graphs -- Follow Recommendation in Communities -- Partner Recommendation -- Social Broker Recommendation -- Conclusion.
Bibliography, Etc. Note
Includes bibliographical references.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Computer science. Recommender systems (Information filtering). Online social networks.
Short cut
URL
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020 ▼a 9783319227351
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050 4 ▼a QA76.76.A65
082 0 4 ▼a 005.56 ▼2 23
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100 1 ▼a Schall, Daniel.
245 1 0 ▼a Social network-based recommender systems ▼h [electronic resource] / ▼c Daniel Schall.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2015.
300 ▼a 1 online resource (xiii, 126 p.) : ▼b ill. (some col.).
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references.
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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Computer science.
650 0 ▼a Recommender systems (Information filtering).
650 0 ▼a Online social networks.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-22735-1
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 005.56 Accession No. E14028452 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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