1st Edition

Recommender Systems A Multi-Disciplinary Approach

Edited By Monideepa Roy, Pushpendu Kar, Sujoy Datta Copyright 2023
    278 Pages 80 B/W Illustrations
    by CRC Press

    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


    Monideepa Roy, Pushpendu Kar, Sujoy Datta