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Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective.
・ISBN 978-0-367-21158-5 hard GB£ 145.99
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| 著者・編者 | Nelakurthi, Arun Reddy / He, Jingrui, |
|---|---|
| シリーズ | (Data-Enabled Engineering) |
| 出版社 | (CRC Press, UK) |
| 出版年月 | 2020 |
| ページ数 | 114 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A01-89711＞ |
解説
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.