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Learning from multiple social networks / Liqiang Nie, Xuemeng Song, and Tat-Seng Chua.

Por: Colaborador(es): Tipo de material: TextoTextoSeries Synthesis lectures on information concepts, retrieval, and services ; # 48.Editor: San Rafael, California : Morgan & Claypool Publishers, 2016Fecha de copyright: ©2016Descripción: xv, 102 páginas : ilustraciones, gráficas, tablas ; 24 cmTipo de contenido:
  • texto
Tipo de medio:
  • sin mediación
Tipo de soporte:
  • volumen
ISBN:
  • 9781627059862
  • 1627059865
  • 1627054243
  • 9781627054249
Tema(s): Clasificación LoC:
  • HM 742 N54.2016
Contenidos:
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.
Resumen: 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 applicationResumen: scenas, 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.
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Part of: Synthesis digital library of engineering and computer science.

Title from PDF title page (viewed on May 13, 2016).

Incluye referencias bibliográficas (páginas 87-100).

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.

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

scenas, 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.