Big Data Recommender Systems

Algorithms, Architectures, Big Data, Security and Trust
Herausgeber: Khalid, Osman; Zomaya, Albert Y; Khan, Samee U

• Gebundenes Buch

Produktbeschreibung

This book combines experimental and theoretical research on big data recommender systems to help computer scientists develop new concepts and methodologies for complex applications. It includes original scientific contributions in the form of theoretical foundations, comparative analysis, surveys, case studies, techniques and tools.

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Produktdetails

• Verlag: Institution of Engineering & Technology
• Seitenzahl: 368
• Erscheinungstermin: 29. August 2019
• Englisch
• Abmessung: 241mm x 161mm x 26mm
• Gewicht: 720g
• ISBN-13: 9781785619755
• ISBN-10: 1785619756
• Artikelnr.: 56156446

Inhaltsangabe

* Chapter 1: Introduction to big data recommender systems - volume 1
* Chapter 2: Theoretical foundations for recommender systems
* Chapter 3: Benchmarking big data recommendation algorithms using
Hadoop orApache Spark
* Chapter 4: Efficient and socio-aware recommendation approaches for
bigdata networked systems
* Chapter 5: Novel hybrid approaches for big data recommendations
* Chapter 6: Deep generative models for recommender systems
* Chapter 7: Recommendation algorithms for unstructured big data such
as text, audio, image and video
* Chapter 8: Deep segregation of plastic (DSP): segregation of plastic
and nonplastic using deep learning
* Chapter 9: Spatiotemporal recommendation with big geo-social
networking data
* Chapter 10: Recommender system for predicting malicious Android
applications
* Chapter 11: Security threats and their mitigation in big data
recommender systems
* Chapter 12: User's privacy in recommendation systems applying online
social network data: a survey and taxonomy
* Chapter 13: Private entity resolution for big data on Apache Spark
using multiple phonetic codes
* Chapter 14: Deep learning architecture for big data analytics in
detecting intrusions and malicious URL