1st Edition
Recommender Systems A Multi-Disciplinary Approach
Recommender Systems: A Multi-Disciplinary Approach presents a multi-disciplinary approach for the development of recommender systems. It explains different types of pertinent algorithms with their comparative analysis and their role for different applications. This book explains the big data behind recommender systems, the marketing benefits, how to make good decision support systems, the role of machine learning and artificial networks, and the statistical models with two case studies. It shows how to design attack resistant and trust-centric recommender systems for applications dealing with sensitive data.
Features of this book:
- 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 and graduate students in computer science, electronics and communication engineering, mathematical science, and data science.
1. Comparison of Different Machine Learning Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach
Subrata Dutta, Manish Kumar, Arindam Giri, Ravi Bhusan Thakur, Sarmistha Neogy, and Keshav Dahal
2. An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies
Debajyoty Banik and Mansheel Agarwal
3. Neural Network-Based Collaborative Filtering for Recommender Systems
Ananya Singh and Debajyoti Banik
4. Recommendation System and Big Data: Its Types and Applications
Shweta Mongia, Tapas Kumar, and Supreet Kaur
5. The Role of Machine Learning /AI in Recommender Systems
N R Saturday, K T Igulu, T P Singh, and F E Onuodu
6. A Recommender System Based on TensorFlow Framework
Hukum Singh Rana and T P Singh
7. A Marketing Approach to Recommender Systems
K T Igulu, T P Singh, F E Onuodu, and N S Agbeb
8. Applied Statistical Analysis in Recommendation Systems
Bikram Pratim Bhuyan and T P Singh
9. An IoT-Enabled Innovative Smart Parking Recommender Approach
Ajanta Das and Soumya Sankar Basu
10. Classification of Road Segments in Intelligent Traffic Management System
Md Ashifuddin Mondal and Zeenat Rehena
11. Facial Gestures-Based Recommender System for Evaluating Online Classes
Anjali Agarwal and Ajanta Das
12. Application of Swarm Intelligence in Recommender Systems
Shriya Singh, Monideepa Roy, Sujoy Datta, and 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, and Pushpendu Kar
Biography
Monideepa Roy, Pushpendu Kar, Sujoy Datta
