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

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Bibliographic Details
Online Access: Full Text (via Morgan & Claypool)
Main Authors: Nie, Liqiang (Author), Song, Xuemeng (Author), Chua, T. S. (Tat-Seng), 1955- (Author)
Format: eBook
Language:English
Published: San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2016.
Series:Synthesis lectures on information concepts, retrieval, and services (Online) ; # 48.
Subjects:
Online social networks > Research.
Internet users > Research.
Interest (Psychology) > Research.
Table of 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.

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