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

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Synopsis
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.
Copyright:
2020

Book Details

Book Quality:
Publisher Quality
Book Size:
98 Pages
ISBN-13:
9781000025361
Related ISBNs:
9780429270352, 9780367211585, 9780367211585, 9781032175782, 9780367211585, 9780429270352
Publisher:
CRC Press
Date of Addition:
Copyrighted By:
N/A
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Technology, Sociology
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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