Big Data Recommender Systems Algorithms, Architectures, Big Data, Security and Trust
Produktdetaljer
Biografisk notat
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 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.
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 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