Social media analytics for user behavior modeling : a task heterogeneity perspective /
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...
| Main Authors: | , |
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| Corporate Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Boca Raton :
CRC Press,
2020
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| Series: | Data-enabled engineering.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | 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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| Physical Description: | 1 online resource (xv, 97 pages) : illustrations |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9781000025408 1000025403 9781000025361 1000025365 9780429270352 0429270356 |