Big Data Recommender Systems: Volume 2: Application Paradigms
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ISBN: 9781785619779
Author/Editor: Osman Khalid
Publisher: IET
Year: 2019
Available on backorder
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.
Additional information
| Weight | 0.861 kg |
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Product Properties
| Year of Publication | 2019 |
|---|---|
| Table of Contents | 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 |
| Author | Osman Khalid |
| ISBN/ISSN | 9781785619779 |
| Binding | Hardback |
| Edition | 1 |
| Publisher | IET |
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