Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective

Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective
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Book Description
In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem.
Features:
  • Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity
  • Presents a detailed study of existing research
  • Provides convergence and complexity analysis of the frameworks
  • Includes algorithms to implement the proposed research work
  • Covers extensive empirical analysis
Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

Content

Chapter 1. Introduction
Chapter 2. Literature Survey
Chapter 3. Social Media for Diabetes Management
Chapter 4. Learning from Task Heterogeneity
Chapter 5. Explainable Transfer Learning
Chapter 6. Conclusion

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