The Resource Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua
Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua
Resource Information
The item Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- Summary
- With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dualheterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date. We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling. This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants
- Language
- eng
- Extent
- 1 PDF (xv, 102 pages)
- Contents
-
- 1. Introduction -- 1.1 Background -- 1.2 Motivation -- 1.3 Challenges -- 1.4 Our solutions and applications -- 1.5 Outline of this book --
- 2. Data gathering and completion -- 2.1 User accounts alignment -- 2.2 Missing data problems -- 2.3 Matrix factorization for data completion -- 2.4 Multiple social networks data completion -- 2.5 Summary --
- 3. Multi-source mono-task learning -- 3.1 Application: volunteerism tendency prediction -- 3.2 Related work -- 3.2.1 Volunteerism and personality analysis -- 3.2.2 Multi-view learning with missing data -- 3.3 Multiple social network learning -- 3.3.1 Notation -- 3.3.2 Problem formulations -- 3.3.3 Optimization -- 3.4 Experimentation -- 3.4.1 Experimental settings -- 3.4.2 Feature extraction -- 3.4.3 Model comparison -- 3.4.4 Data completion comparison -- 3.4.5 Feature comparison -- 3.4.6 Source comparison -- 3.4.7 Size varying of positive samples -- 3.4.8 Complexity discussion -- 3.5 Summary --
- 4. Mono-source multi-task learning -- 4.1 Application: user interest inference from mono-source -- 4.2 Related work -- 4.2.1 Clustered multi-task learning -- 4.2.2 User interest mining -- 4.3 Efficient clustered multi-task learning -- 4.3.1 Notation -- 4.3.2 Problem formulation -- 4.3.3 Grouping structure learning -- 4.3.4 Efficient clustered multi-task learning -- 4.4 Experimentation -- 4.4.1 Experimental settings -- 4.4.2 Feature extraction -- 4.4.3 Evaluation metric -- 4.4.4 Parameter tuning -- 4.4.5 Model comparison -- 4.4.6 Necessity of structure learning -- 4.5 Summary --
- 5. Multi-source multi-task learning -- 5.1 Application: user interest inference from multi-source -- 5.2 Related work -- 5.3 Multi-source multi-task learning -- 5.3.1 Notation -- 5.3.2 Problem formulations -- 5.3.3 Optimization -- 5.3.4 Construction of interest tree structure -- 5.4 Experiments -- 5.4.1 Experimental settings -- 5.4.2 Model comparison -- 5.4.3 Source comparison -- 5.4.4 Complexity discussion -- 5.5 Summary --
- 6. Multi-source multi-task learning with feature selection -- 6.1 Application: user attribute learning from multimedia data -- 6.2 Related work -- 6.3 Data construction -- 6.3.1 Data crawling strategy -- 6.3.2 Ground truth construction -- 6.4 Multi-source multi-task learning with Fused Lasso -- 6.5 Optimization -- 6.6 Experiments -- 6.6.1 Experimental settings -- 6.6.2 Feature extraction -- 6.6.3 Overall model evaluation -- 6.6.4 Component-wise analysis -- 6.6.5 Source integration -- 6.6.6 Parameter tuning -- 6.6.7 Computational analysis -- 6.7 Other application -- 6.8 Summary --
- 7. Research frontiers -- Bibliography -- Authors' biographies
- Isbn
- 9781627059862
- Label
- Learning from multiple social networks
- Title
- Learning from multiple social networks
- Statement of responsibility
- Liqiang Nie, Xuemeng Song, and Tat-Seng Chua
- Language
- eng
- Summary
- With the proliferation of social network services, more and more social users, such as individuals and organizations, are simultaneously involved in multiple social networks for various purposes. In fact, multiple social networks characterize the same social users from different perspectives, and their contexts are usually consistent or complementary rather than independent. Hence, as compared to using information from a single social network, appropriate aggregation of multiple social networks offers us a better way to comprehensively understand the given social users. Learning across multiple social networks brings opportunities to new services and applications as well as new insights on user online behaviors, yet it raises tough challenges: (1) How can we map different social network accounts to the same social users? (2) How can we complete the item-wise and block-wise missing data? (3) How can we leverage the relatedness among sources to strengthen the learning performance? And (4) How can we jointly model the dualheterogeneities: multiple tasks exist for the given application and each task has various features from multiple sources? These questions have been largely unexplored to date. We noticed this timely opportunity, and in this book we present some state-of-the-art theories and novel practical applications on aggregation of multiple social networks. In particular, we first introduce multi-source dataset construction. We then introduce how to effectively and efficiently complete the item-wise and block-wise missing data, which are caused by the inactive social users in some social networks. We next detail the proposed multi-source mono-task learning model and its application in volunteerism tendency prediction. As a counterpart, we also present a mono-source multi-task learning model and apply it to user interest inference. We seamlessly unify these models with the so-called multi-source multi-task learning, and demonstrate several application scenarios, such as occupation prediction. Finally, we conclude the book and figure out the future research directions in multiple social network learning, including the privacy issues and source complementarity modeling. This is preliminary research on learning from multiple social networks, and we hope it can inspire more active researchers to work on this exciting area. If we have seen further it is by standing on the shoulders of giants
- Cataloging source
- CaBNVSL
- http://library.link/vocab/creatorName
- Nie, Liqiang
- Dewey number
- 302.30285
- Illustrations
- illustrations
- Index
- no index present
- LC call number
- HM742
- LC item number
- .N545 2016
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- abstracts summaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorDate
- 1955-
- http://library.link/vocab/relatedWorkOrContributorName
-
- Song, Xuemeng.
- Chua, T. S.
- http://library.link/vocab/subjectName
-
- Online social networks
- Internet users
- Interest (Psychology)
- Target audience
-
- adult
- specialized
- Label
- Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua
- Bibliography note
- Includes bibliographical references (pages 87-100)
- Carrier category
- online resource
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type MARC source
- rdacontent
- Contents
-
- 1. Introduction -- 1.1 Background -- 1.2 Motivation -- 1.3 Challenges -- 1.4 Our solutions and applications -- 1.5 Outline of this book --
- 2. Data gathering and completion -- 2.1 User accounts alignment -- 2.2 Missing data problems -- 2.3 Matrix factorization for data completion -- 2.4 Multiple social networks data completion -- 2.5 Summary --
- 3. Multi-source mono-task learning -- 3.1 Application: volunteerism tendency prediction -- 3.2 Related work -- 3.2.1 Volunteerism and personality analysis -- 3.2.2 Multi-view learning with missing data -- 3.3 Multiple social network learning -- 3.3.1 Notation -- 3.3.2 Problem formulations -- 3.3.3 Optimization -- 3.4 Experimentation -- 3.4.1 Experimental settings -- 3.4.2 Feature extraction -- 3.4.3 Model comparison -- 3.4.4 Data completion comparison -- 3.4.5 Feature comparison -- 3.4.6 Source comparison -- 3.4.7 Size varying of positive samples -- 3.4.8 Complexity discussion -- 3.5 Summary --
- 4. Mono-source multi-task learning -- 4.1 Application: user interest inference from mono-source -- 4.2 Related work -- 4.2.1 Clustered multi-task learning -- 4.2.2 User interest mining -- 4.3 Efficient clustered multi-task learning -- 4.3.1 Notation -- 4.3.2 Problem formulation -- 4.3.3 Grouping structure learning -- 4.3.4 Efficient clustered multi-task learning -- 4.4 Experimentation -- 4.4.1 Experimental settings -- 4.4.2 Feature extraction -- 4.4.3 Evaluation metric -- 4.4.4 Parameter tuning -- 4.4.5 Model comparison -- 4.4.6 Necessity of structure learning -- 4.5 Summary --
- 5. Multi-source multi-task learning -- 5.1 Application: user interest inference from multi-source -- 5.2 Related work -- 5.3 Multi-source multi-task learning -- 5.3.1 Notation -- 5.3.2 Problem formulations -- 5.3.3 Optimization -- 5.3.4 Construction of interest tree structure -- 5.4 Experiments -- 5.4.1 Experimental settings -- 5.4.2 Model comparison -- 5.4.3 Source comparison -- 5.4.4 Complexity discussion -- 5.5 Summary --
- 6. Multi-source multi-task learning with feature selection -- 6.1 Application: user attribute learning from multimedia data -- 6.2 Related work -- 6.3 Data construction -- 6.3.1 Data crawling strategy -- 6.3.2 Ground truth construction -- 6.4 Multi-source multi-task learning with Fused Lasso -- 6.5 Optimization -- 6.6 Experiments -- 6.6.1 Experimental settings -- 6.6.2 Feature extraction -- 6.6.3 Overall model evaluation -- 6.6.4 Component-wise analysis -- 6.6.5 Source integration -- 6.6.6 Parameter tuning -- 6.6.7 Computational analysis -- 6.7 Other application -- 6.8 Summary --
- 7. Research frontiers -- Bibliography -- Authors' biographies
- Control code
- 201603ICR048
- Dimensions
- unknown
- Extent
- 1 PDF (xv, 102 pages)
- File format
- multiple file formats
- Form of item
- online
- Isbn
- 9781627059862
- Media category
- electronic
- Media MARC source
- isbdmedia
- Other control number
- 10.2200/S00714ED1V01Y201603ICR048
- Other physical details
- illustrations.
- Reformatting quality
- access
- Specific material designation
- remote
- System details
- System requirements: Adobe Acrobat Reader
- Label
- Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua
- Bibliography note
- Includes bibliographical references (pages 87-100)
- Carrier category
- online resource
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type MARC source
- rdacontent
- Contents
-
- 1. Introduction -- 1.1 Background -- 1.2 Motivation -- 1.3 Challenges -- 1.4 Our solutions and applications -- 1.5 Outline of this book --
- 2. Data gathering and completion -- 2.1 User accounts alignment -- 2.2 Missing data problems -- 2.3 Matrix factorization for data completion -- 2.4 Multiple social networks data completion -- 2.5 Summary --
- 3. Multi-source mono-task learning -- 3.1 Application: volunteerism tendency prediction -- 3.2 Related work -- 3.2.1 Volunteerism and personality analysis -- 3.2.2 Multi-view learning with missing data -- 3.3 Multiple social network learning -- 3.3.1 Notation -- 3.3.2 Problem formulations -- 3.3.3 Optimization -- 3.4 Experimentation -- 3.4.1 Experimental settings -- 3.4.2 Feature extraction -- 3.4.3 Model comparison -- 3.4.4 Data completion comparison -- 3.4.5 Feature comparison -- 3.4.6 Source comparison -- 3.4.7 Size varying of positive samples -- 3.4.8 Complexity discussion -- 3.5 Summary --
- 4. Mono-source multi-task learning -- 4.1 Application: user interest inference from mono-source -- 4.2 Related work -- 4.2.1 Clustered multi-task learning -- 4.2.2 User interest mining -- 4.3 Efficient clustered multi-task learning -- 4.3.1 Notation -- 4.3.2 Problem formulation -- 4.3.3 Grouping structure learning -- 4.3.4 Efficient clustered multi-task learning -- 4.4 Experimentation -- 4.4.1 Experimental settings -- 4.4.2 Feature extraction -- 4.4.3 Evaluation metric -- 4.4.4 Parameter tuning -- 4.4.5 Model comparison -- 4.4.6 Necessity of structure learning -- 4.5 Summary --
- 5. Multi-source multi-task learning -- 5.1 Application: user interest inference from multi-source -- 5.2 Related work -- 5.3 Multi-source multi-task learning -- 5.3.1 Notation -- 5.3.2 Problem formulations -- 5.3.3 Optimization -- 5.3.4 Construction of interest tree structure -- 5.4 Experiments -- 5.4.1 Experimental settings -- 5.4.2 Model comparison -- 5.4.3 Source comparison -- 5.4.4 Complexity discussion -- 5.5 Summary --
- 6. Multi-source multi-task learning with feature selection -- 6.1 Application: user attribute learning from multimedia data -- 6.2 Related work -- 6.3 Data construction -- 6.3.1 Data crawling strategy -- 6.3.2 Ground truth construction -- 6.4 Multi-source multi-task learning with Fused Lasso -- 6.5 Optimization -- 6.6 Experiments -- 6.6.1 Experimental settings -- 6.6.2 Feature extraction -- 6.6.3 Overall model evaluation -- 6.6.4 Component-wise analysis -- 6.6.5 Source integration -- 6.6.6 Parameter tuning -- 6.6.7 Computational analysis -- 6.7 Other application -- 6.8 Summary --
- 7. Research frontiers -- Bibliography -- Authors' biographies
- Control code
- 201603ICR048
- Dimensions
- unknown
- Extent
- 1 PDF (xv, 102 pages)
- File format
- multiple file formats
- Form of item
- online
- Isbn
- 9781627059862
- Media category
- electronic
- Media MARC source
- isbdmedia
- Other control number
- 10.2200/S00714ED1V01Y201603ICR048
- Other physical details
- illustrations.
- Reformatting quality
- access
- Specific material designation
- remote
- System details
- System requirements: Adobe Acrobat Reader
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Learning-from-multiple-social-networks-Liqiang/IjaZqS-7SVA/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Learning-from-multiple-social-networks-Liqiang/IjaZqS-7SVA/">Learning from multiple social networks, Liqiang Nie, Xuemeng Song, and Tat-Seng Chua</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
