Big Data Recommender Systems: Volume 2
Application Paradigms
Published on 29. August 2019
Book
Hardback
520 pages
978-1-78561-977-9 (ISBN)
Description
First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges.
Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures.
Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 239 mm
Width: 160 mm
Thickness: 30 mm
Weight
862 gr
ISBN-13
978-1-78561-977-9 (9781785619779)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Osman Khalid is assistant professor at the department of computer sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan. His research interests include recommender systems, trust and reputation system, disaster response systems, delay tolerant networks, wireless networks, and fog computing.
Samee U. Khan is associate professor of electrical and computer engineering at the North Dakota State University, USA. His research interests include optimization, robustness, and security of systems.
Albert Y. Zomaya is chair professor of high performance computing & networking and Australian research council professorial fellow in the School of Information Technologies, The University of Sydney, Australia. He is also the director of the Centre for Distributed and High Performance Computing.
Samee U. Khan is associate professor of electrical and computer engineering at the North Dakota State University, USA. His research interests include optimization, robustness, and security of systems.
Albert Y. Zomaya is chair professor of high performance computing & networking and Australian research council professorial fellow in the School of Information Technologies, The University of Sydney, Australia. He is also the director of the Centre for Distributed and High Performance Computing.
Content
Chapter 1: Introduction to big data recommender systems - volume 2
Chapter 2: Deep neural networks meet recommender systems
Chapter 3: Cold-start solutions for recommendation systems
Chapter 4: Performance metrics for traditional and context-aware big data recommender systems
Chapter 5: Mining urban lifestyles: urban computing, human behavior and recommender systems
Chapter 6: Embedding principal component analysis inference in expert sensors for big data applications
Chapter 7: Decision support system to detect hidden pathologies of stroke: the CIPHER project
Chapter 8: Big data analytics for smart grids
Chapter 9: Internet of Things and big data recommender systems to support Smart Grid
Chapter 10: Recommendation techniques and their applications to the delivery of an online bibliotherapy
Chapter 11: Stream processing in Big Data for e-health care
Chapter 12: How Hadoop and Spark benchmarking algorithms can improve remote health monitoring and data management platforms?
Chapter 13: Extracting and understanding user sentiments for big data analytics in big business brands
Chapter 14: A recommendation system for allocating video resources in multiple partitions
Chapter 15: A mood-sensitive recommendation system in social sensing
Chapter 16: The paradox of opinion leadership and recommendation culture in Chinese online movie reviews
Chapter 17: Real-time optimal route recommendations using MapReduce
Chapter 18: Investigation of relationships between high-level user contexts and mobile application usage
Chapter 19: Machine learning and stock recommendation
Chapter 20: The role of smartphone in recommender systems: opportunities and challenges
Chapter 21: Graph-based recommendations: from data representation to feature extraction and application
Chapter 22: AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning
Chapter 2: Deep neural networks meet recommender systems
Chapter 3: Cold-start solutions for recommendation systems
Chapter 4: Performance metrics for traditional and context-aware big data recommender systems
Chapter 5: Mining urban lifestyles: urban computing, human behavior and recommender systems
Chapter 6: Embedding principal component analysis inference in expert sensors for big data applications
Chapter 7: Decision support system to detect hidden pathologies of stroke: the CIPHER project
Chapter 8: Big data analytics for smart grids
Chapter 9: Internet of Things and big data recommender systems to support Smart Grid
Chapter 10: Recommendation techniques and their applications to the delivery of an online bibliotherapy
Chapter 11: Stream processing in Big Data for e-health care
Chapter 12: How Hadoop and Spark benchmarking algorithms can improve remote health monitoring and data management platforms?
Chapter 13: Extracting and understanding user sentiments for big data analytics in big business brands
Chapter 14: A recommendation system for allocating video resources in multiple partitions
Chapter 15: A mood-sensitive recommendation system in social sensing
Chapter 16: The paradox of opinion leadership and recommendation culture in Chinese online movie reviews
Chapter 17: Real-time optimal route recommendations using MapReduce
Chapter 18: Investigation of relationships between high-level user contexts and mobile application usage
Chapter 19: Machine learning and stock recommendation
Chapter 20: The role of smartphone in recommender systems: opportunities and challenges
Chapter 21: Graph-based recommendations: from data representation to feature extraction and application
Chapter 22: AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning