Name: Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective (Data-Enabled Engineering) (en Inglés)
Price: 143.78 USD
Availability: InStock
Author: Arun Reddy Nelakurthi; Jingrui He
ISBN: 9780367211585

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

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

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Arun Reddy Nelakurthi; Jingrui He

Idioma

Inglés

Encuadernación

Tapa Dura

ISBN13

9780367211585

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

Arun Reddy Nelakurthi; Jingrui He (Autor) · Crc Pr Inc · Tapa Dura

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Reseña del libro "Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective (Data-Enabled Engineering) (en Inglés)"

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.