Social Media Analytics for User Behavior Modeling

<p><em><strong>Winner of the Outstanding Academic Title recognition by Choice for the 2020 OAT Awards.</strong></em></p><p><em>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.</em></p><p>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. </p><p>Features:</p><ul> <p> </p> <li>Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity</li> <p> </p> <li>Presents a detailed study of existing research</li> <p> </p> <li>Provides convergence and complexity analysis of the frameworks</li> <p> </p> <li>Includes algorithms to implement the proposed research work</li> <p> </p> <li>Covers extensive empirical analysis</li> </ul><p>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.</p>