Social Media Analytics for User Behavior Modeling-A Task Heterogeneity Perspective 1st Edition by Arun Reddy Nelakurthi, Jingrui He ISBN 978-1032175782 1032175788

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Product details: 

ISBN 10:  1032175788

ISBN 13: 978-1032175782

Author: Arun Reddy Nelakurthi, Jingrui He

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 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.

Table of contents: 

1. Introduction

2. Related Work

3. User-Guided Cross-Domain Sentiment Classification

4. Similar Actor Recommendation

5. Source-Free Domain Adaptation of the Off-the-Shelf Classifier

6. Social Media for Diabetes Management

7. Conclusion and Future Work

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