Actions for Big data recommender systems. Volume 1, Algorithms, Architectures, Big Data, Security and Trust

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Big data recommender systems. Volume 1, Algorithms, Architectures, Big Data, Security and Trust / edited by Osman Khalid, Samee U. Khan and Albert Y. Zomaya

Published
Stevenage : IET, 2019.
Copyright Date
©2019
Physical Description
1 online resource (xiv, 352 pages).
Additional Creators
Khalid, Osman, Khan, Samee Ullah, and Zomaya, Albert Y.
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Contents
Intro -- Contents -- Foreword -- 1. Introduction to big data recommender systems-volume 1 / Osman Khalid, Faisal Rehman, Samee U. Khan, and Albert Y. Zomaya -- 1.1 Background -- 1.2 About the book -- Acknowledgments -- References -- 2. Theoretical foundations for recommender systems / Mirza Zaeem Baig, Hasina Khatoon, Syeda Saleha Raza, and Muhammad Qasim Pasta -- 2.1 Introduction -- 2.2 Applications of RSs -- 2.3 Algorithms and theoretical foundations of RSs -- 2.4 Problems related to RSs -- References, 3. Benchmarking big data recommendation algorithms using Hadoop or Apache Spark / Dinesh Kumar Saini, Kashif Zia, and Arshad Muhammad -- 3.1 Introduction -- 3.2 Big data -- 3.3 Apache Spark -- 3.4 Recommender systems -- 3.5 Systems based on nature-inspired algorithms -- 3.6 Benchmarking: big data benchmarking -- 3.7 Summary -- References -- 4. Efficient and socio-aware recommendation approaches for big data networked systems / Vasileios Karyotis, Margarita Vitoropoulou, Nikos Kalatzis, Ioanna Roussaki, and Symeon Papavassiliou -- 4.1 Introduction, 4.2 Background on recommendation systems and social network analysis -- 4.3 Socio-aware recommendation systems -- 4.4 Qualitative comparison -- 4.5 Open problems and conclusion -- References -- 5. Novel hybrid approaches for big data recommendations / Abdul Kader Saiod and Darelle van Greunen -- 5.1 Introduction -- 5.2 Context -- 5.3 The big data architecture -- 5.4 Different approaches to handle big data -- 5.5 Complexity and issues of big DI -- 5.6 Big DI using HAs based on Fuzzy-Ontology -- 5.7 Developing approaches for the crisp ontology -- 5.8 Developing HAs for Fuzzy-Ontology, 5.9 Extracting the big data key business functions for the proposed HAs based on Fuzzy-Ontology -- 5.10 Identify the specification for the purpose HIDAs for big data -- 5.11 Real-world project: hypertension-specific diagnosis based on HIDAs -- 5.12 Mathematical simulation of hypertension diagnosis based on Markov chain probability model -- 5.13 Analysis of result -- 5.14 Conclusion -- References -- 6. Deep generative models for recommender systems / Vineeth Rakesh, Suhang Wang, and Huan Liu -- 6.1 Introduction -- 6.2 Generative models -- 6.3 Deep learning for recommender systems, and 6.4 Deep generative models -- 6.5 Summary -- References -- 7. Recommendation algorithms for unstructured big data such as text, audio, image and video / Madjid Khalilian, Mahshid Alsadat Ehsaei, and Saloomeh Taheri Fard -- 7.1 Recommender methods -- 7.2 Big data analytic -- 7.3 Recommender systems: challenges and limitations -- 7.4 Summary -- References -- 8. Deep segregation of plastic (DSP): segregation of plastic and nonplastic using deep learning / K. Sreelakshmi, R. Vinayakumar, and K.P. Soman -- 8.1 Introduction -- 8.2 Related work -- 8.3 Deep learning -- 8.4 Scalable architecture
Summary
Volume 1 covers aspects related to recommender systems preliminaries, algorithms, and architectures; recommendation approaches for big data; and trust and security measures for recommender systems.
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ISBN
9781785619762 (electronic book)
1785619764 (electronic book)
9781785619755 (hardcover)
1785619756 (hardcover)
View MARC record | catkey: 43290340