Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective (Data-Enabled Engineering)

Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards.The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community.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 heterogeneityPresents a detailed study of existing researchProvides convergence and complexity analysis of the frameworksIncludes algorithms to implement the proposed research workCovers extensive empirical analysisSocial 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. Read more