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OverviewThis 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. Full Product DetailsAuthor: Osman Khalid (Assistant Professor, COMSATS Institute of Information Technology, Department of Computer Sciences, Pakistan) , Samee U. Khan (Associate Professor, North Dakota State University, USA) , Albert Y. Zomaya (Chair Professor, The University of Sydney, Australia)Publisher: Institution of Engineering and Technology Imprint: Institution of Engineering and Technology ISBN: 9781785619779ISBN 10: 1785619772 Pages: 520 Publication Date: 29 August 2019 Audience: College/higher education , Professional and scholarly , Tertiary & Higher Education , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsChapter 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 learningReviewsAuthor InformationOsman 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. Tab Content 6Author Website:Countries AvailableAll regions |
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