Recommender Systems: A Multi-Disciplinary Approach

Recommender Systems: A Multi-Disciplinary Approach (Intelligent Systems) 1st Edition
by Monideepa Roy (Editor), Pushpendu Kar (Editor), Sujoy Datta (Editor)
Publisher Finelybook 出版社: CRC Press; 1st edition (June 19, 2023)
Language 语言: English
pages 页数: 280 pages
ISBN-10 书号: 1032333219
ISBN-13 书号: 9781032333212

Book Description
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 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

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