Big Data Recommender Systems: 2 Volume Set
Published on 1. January 1900
Book
888 pages
978-1-78561-979-3 (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 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.
Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-78561-979-3 (9781785619793)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its 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
Volume 1
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
Volume 2
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 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
Volume 2
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