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

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

English | 2020 | ISBN: 978-0367211585 | 114 Pages | PDF | 10 MB

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.

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