A Multi-Disciplinary Approach
- Available for pre-order on May 29, 2023. Item will ship after June 19, 2023
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Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for development of Recommender Systems. It explains different types of pertinent algorithms with their comparative analysis, and their role for different applications. It explains Big Data behind Recommender System, marketing benefits, making good decision support systems, role of machine learning and artificial networks, and statistical models with two case studies. It shows how to design attack resistant and trust centric recommender systems for applications dealing with sensitive data.
- Identifies and describes recommender systems for practical uses
- Describes how to design, train, and evaluate a recommendation algorithm
- Explains migration from a recommendation model to a live system with users
- Describes utilization of the data collected from a recommender system to understand the user preferences
- Addresses the security aspects and ways to deal with possible attacks to build a robust system
This book is aimed at researchers, graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
Table of Contents
1. Comparison of Different Machine Learning Algorithms to classify twitter data: A simulation-based Approach.
Subrata Dutta, Manish Kumar, Arindam Giri, Ravi Bhusan Thakur, Sarmistha Neogy, Keshav Dahal
2. An End-to-end Comparison Among Contemporary Content-based Recommendation Methodologies.
Debajyoty Banik, Mansheel Agarwal
3. Neural Network Based Collaborative Filtering for Recommender Systems.
Debajyoti Banik, Ananya Singh
4. Recommendation System and Bigdata: Its Types and Applications.
Shweta Mongia, Tapas Kumar, Supreet Kaur
5. The Role of Machine Learning /AI in Recommender Systems.
N.R. Saturday, K.T. Igulu, T.P. Singh, F.E. Onuodu
6. Developing a Recommender System using TensorFlow.
Hukum Singh Rana, T P Singh
7. A Marketing Approach to Recommender Systems.
K.T. Igulu, T.P. Singh, F.E. Onuodu & N.S. Agbeb
8. Applied Statistical Analysis in Recommendation Systems.
Bikram Pratim Bhuyan, T P Singh
9. An IoT enabled Innovative Smart Parking Recommender Approach.
Ajanta Das, Soumya Sankar Basu
10. Classification of Road Segments in Intelligent Traffic Management System.
Md Ashifuddin Mondal, Zeenat Rehena
11. Facial Gestures based Recommender System for Evaluating Online Classes.
Anjali Agarwal and Ajanta Das
12. Application of Swarm Intelligence Techniques in Recommender Systems.
Shriya Singh, Monideepa Roy, Sujoy Datta, Pushpendu Kar
13. Application of machine learning techniques in the development of neighbourhood-based robust recommender systems.
Swarup Chattopadhyay, Anjan Chowdhury, and Kuntal Ghosh
14. Recommendation Systems for Choosing Online Learning Resources - A Hands on Approach.
Arkajit Saha, Shreya Dey, Monideepa Roy, Sujoy Datta, Pushpendu Kar
Dr. Monideepa Roy did her Bachelors & Masters in Mathematics from IIT Kharagpur, and her PhD in CSE from Jadavpur University. Currently she is working as an Associate Professor at KIIT Deemed University, Bhubaneswar since the last 11 years. Her areas of interest include Remote Healthcare, Mobile Computing, Cognitive WSNs, Remote Sensing, Recommender Systems, Sparse Approximations,and Artifical Neural Networks. At present she has seven research scholars working with her in the above areas and two more have successfully defended their theses under her guidance. She has several publications in reputed conferences and journals. She has been the Organizing Chair of the first two editions of the International Conference on Computational Intelligence and Networks CINE 2015 and 2016, ICMC 2019 and has organised several workshops and seminars She also has several book chapter publications in various reputed publication houses as well as an edited book under Taylor and Francis. She has also been an invited speaker for several workshops and confererences in Machine Learning and Recommendation Systems. She is also a reviewer for several international journals and conferences.
Dr. Pushpendu Kar is an Assistant Professor in the School of Computer Science at the University of Nottingham Ningbo China (China campus of the University of Nottingham UK). Before this, he was a Research Fellow in the Department of ICT and Natural Sciences at Norwegian University of Science and Technology (NTNU), Norway, the Department of Electrical & Computer Engineering at National University of Singapore (NUS), and the Energy Research Institute at Nanyang Technological University (NTU), Singapore. He has completed all his PhD, Masters of Engineering, and Bachelor of Technology in Computer Science and Engineering. He also completed Sun Certified Java Programmer (SCJP) 5.0, one professional course on Hardware & Networking, two professional courses on JAVA-J2EE, Finishing School Program from National Institute of Technology Durgapur, India, and UGC sponsored refreshers course from Jadavpur University, India. Dr. Kar went for a research visit to Inria Paris, France. Dr. Kar was awarded the prestigious Erasmus Mundus Postdoctoral Fellowship of European Commission, ERCIM Alain Bensoussan Fellowship of European Union, and SERB OPD Fellowship of Department of Science and Technology, Government of India. He received the 2020 IEEE Systems Journal (2020 I.F.: 4.463) Best Paper Award. This is one of the seven papers out of 793 papers [top 1%] that have received the award. Dr. Kar is an IEEE Senior Member. He received four research grants, three of them as Principal Investigator and one as Co-Principal Investigator for conducting research-based projects. He also received several travel grants to attend conferences and doctoral colloquiums. Dr. Kar has more than 11 years of teaching and research experience, including in a couple of highly reputed organizations around the world. He worked as a software professional in IBM for one and a half years. Dr. Kar is the author of more than 40 scholarly research papers, which have been published in reputed journals. He is also an inventor of four patents. He has participated in the program committee of several conferences, worked as a team member to organize short-term courses, and delivered few keynote speeches and invited talks. He is a regular reviewer of journals and conferences. He has a research interest in Wireless Sensor Networks, Internet of Things, Content-Centric Networking, Big Data, and Blockchain.
Sujoy Datta has done his M.Tech. from IIT Kharagpur. Currently he is working as an Assistant Professor in the School of Computer Engineering, KIIT Deemed University since the last eleven years. His areas of research include Wireless networks, Computer Security, Elliptic curve cryptography and neural networks, Remote Healthcare and Recommender Systems. He has several publications in various conferences and journals. He has co-organised several workshops and international conferences as well as several workshops and seminars. He also has several book chapter publications in Springer as well as an edited book by Taylor and Francis.