Big Data Recommender Systems Application Paradigms
Produktdetaljer
Om bidragsyterne
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.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.
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.
- 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