Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Social Network-Based Recommender Systems
~
SpringerLink (Online service)
Social Network-Based Recommender Systems
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Social Network-Based Recommender Systems/ by Daniel Schall.
Author:
Schall, Daniel.
Description:
XIII, 126 p. 42 illus., 35 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Application software. -
Online resource:
https://doi.org/10.1007/978-3-319-22735-1
ISBN:
9783319227351
Social Network-Based Recommender Systems
Schall, Daniel.
Social Network-Based Recommender Systems
[electronic resource] /by Daniel Schall. - 1st ed. 2015. - XIII, 126 p. 42 illus., 35 illus. in color.online resource.
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
Standard No.: 10.1007/978-3-319-22735-1doiSubjects--Topical Terms:
528147
Application software.
LC Class. No.: QA76.76.A65
Dewey Class. No.: 005.7
Social Network-Based Recommender Systems
LDR
:02596nam a22004095i 4500
001
963101
003
DE-He213
005
20200706072735.0
007
cr nn 008mamaa
008
201211s2015 gw | 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
035
$a
978-3-319-22735-1
050
4
$a
QA76.76.A65
072
7
$a
UNH
$2
bicssc
072
7
$a
COM032000
$2
bisacsh
072
7
$a
UNH
$2
thema
072
7
$a
UDBD
$2
thema
082
0 4
$a
005.7
$2
23
100
1
$a
Schall, Daniel.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1069174
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.
$3
528147
650
0
$a
Graph theory.
$3
527884
650
1 4
$a
Information Systems Applications (incl. Internet).
$3
881699
650
2 4
$a
Graph Theory.
$3
786670
650
2 4
$a
Computer Appl. in Social and Behavioral Sciences.
$3
669920
710
2
$a
SpringerLink (Online service)
$3
593884
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.org/10.1007/978-3-319-22735-1
912
$a
ZDB-2-SCS
912
$a
ZDB-2-SXCS
950
$a
Computer Science (SpringerNature-11645)
950
$a
Computer Science (R0) (SpringerNature-43710)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login