Big data recommender systems. Volume 1, Algorithms, architectures, big data, security and trust / edited by Osman Khalid, Samee U. Khan and Albert Y. Zomaya.

Format:

Book

Contributor:

Khalid, Osman, editor.

Khan, Samee Ullah, editor.

Zomaya, Albert Y., editor.

Series:

Professional applications of computing series ; 35.

IET professional applications of computing series ; 35

Language:

English

Subjects (All):

Recommender systems (Information filtering).

Physical Description:

1 online resource (xiv, 352 pages).

Edition:

1st ed.

Place of Publication:

London, England : The Institution of Engineering and Technology, [2019]

Summary:

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.

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

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

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

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

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

6.4 Deep generative models

6.5 Summary

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

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

8.5 Software framework

8.6 Software and packages

8.7 Hardware components used

8.8 Hardware setup for segregation

8.9 Experiments and observation

8.10 Conclusion and future work

Appendix A

Appendix B

9. Spatiotemporal recommendation with big geo-social networking data / Weiqing Wang and Hongzhi Yin

9.1 Introduction

9.2 Preliminaries about SAGE

9.3 Spatial-temporal SAGE model

9.4 Spatial item recommendation using ST-SAGE

9.5 Experiments

9.6 Related work

9.7 Conclusion

10. Recommender system for predicting malicious Android applications / Tanya Gera, Jaiteg Singh, Deepak Thakur, and Rajinder Sandhu

10.1 Background

10.2 The proposed recommender system for mobile application risk reduction

10.3 Conclusion

References.

11. Security threats and their mitigation in big data recommender systems / Madjid Khalilian, Maryam Fathi Ahmadsaraei, and Lida Farajpour

11.1 Introduction

11.2 Security issues and approaches in HDFS architecture

11.3 Big data recommender system attacks

11.4 Recommender algorithms

11.5 Attack response and system robustness

11.6 Conclusion

12. User's privacy in recommendation systems applying online social network data: a survey and taxonomy / Erfan Aghasian, Saurabh Garg, and James Montgomery

12.1 Introduction

12.2 Recommender systems and techniques: privacy of online social network data

12.3 Taxonomy of privacy

12.4 Privacy preservation in recommender systems

12.5 Conclusion and future directions

13. Private entity resolution for big data onApache Spark using multiple phonetic codes / Alexandros Karakasidis and Georgia Koloniari

13.1 Introduction

13.2 Related work

13.3 Problem formulation and background

13.4 A parallel privacy preserving phonetics matching protocol

13.5 Empirical evaluation

13.6 Conclusions and future work

14. Deep learning architecture for big data analytics in detecting intrusions and malicious URL / N.B. Harikrishnan, R. Vinayakumar, K.P. Soman, Prabaharan Poornachandran, B. Annappa, and Mamoun Alazab

14.1 Introduction

14.2 Related works

14.3 Background

14.4 Intrusion detection

14.5 Intrusion detection (ID) using multidimensional zoom(M-ZOOM) framework

14.6 Phishing URL detection

14.7 Proposed architecture for machine learning based cybersecurity

14.8 Conclusion and future work

Index.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

1-83724-779-X

1-78561-976-4

OCLC:

1134855185