Big Data Recommender Systems

Application Paradigms

AvOsman, Khalid,Osman Khalid

Inbunden, Engelska, 2019

Del i serien Computing and Networks

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2019-08-29
Mått:156 x 234 x 30 mm
Vikt:862 g
Format:Inbunden
Språk:Engelska
Serie:Computing and Networks
Antal sidor:520
Förlag:Institution of Engineering and Technology
ISBN:9781785619779

Utforska kategorier

Nätverk och kommunikation inom Data och IT

Mer om författaren

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.

Innehållsförteckning

Chapter 1: Introduction to big data recommender systems - volume 2Chapter 2: Deep neural networks meet recommender systemsChapter 3: Cold-start solutions for recommendation systemsChapter 4: Performance metrics for traditional and context-aware big data recommender systemsChapter 5: Mining urban lifestyles: urban computing, human behavior and recommender systemsChapter 6: Embedding principal component analysis inference in expert sensors for big data applicationsChapter 7: Decision support system to detect hidden pathologies of stroke: the CIPHER projectChapter 8: Big data analytics for smart gridsChapter 9: Internet of Things and big data recommender systems to support Smart GridChapter 10: Recommendation techniques and their applications to the delivery of an online bibliotherapyChapter 11: Stream processing in Big Data for e-health careChapter 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 brandsChapter 14: A recommendation system for allocating video resources in multiple partitionsChapter 15: A mood-sensitive recommendation system in social sensingChapter 16: The paradox of opinion leadership and recommendation culture in Chinese online movie reviewsChapter 17: Real-time optimal route recommendations using MapReduceChapter 18: Investigation of relationships between high-level user contexts and mobile application usageChapter 19: Machine learning and stock recommendationChapter 20: The role of smartphone in recommender systems: opportunities and challengesChapter 21: Graph-based recommendations: from data representation to feature extraction and applicationChapter 22: AmritaDGA: a comprehensive data set for domain generation algorithms (DGAs) based domain name detection systems and application of deep learning