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Social Network-Based Recommender Systems
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 'socia...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 005 | 20220118212522.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150923s2015 sz | s |||| 0|eng d | ||
| 020 | |a 9783319227351 |9 978-3-319-22735-1 | ||
| 024 | 7 | |a 10.1007/978-3-319-22735-1 |2 doi | |
| 050 | 4 | |a QA76.76.A65 | |
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| 100 | 1 | |a Schall, Daniel. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Social Network-Based Recommender Systems |h [electronic resource] / |c by Daniel Schall. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a XIII, 126 p. 42 illus., 35 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 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 Application software. | |
| 650 | 0 | |a Graph theory. | |
| 650 | 0 | |a Social sciences-Data processing. | |
| 650 | 1 | 4 | |a Computer and Information Systems Applications. |
| 650 | 2 | 4 | |a Graph Theory. |
| 650 | 2 | 4 | |a Computer Application in Social and Behavioral Sciences. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319227344 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319227368 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319372297 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-22735-1 |z Texto Completo |
| 912 | |a ZDB-2-SCS | ||
| 912 | |a ZDB-2-SXCS | ||
| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||