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.
Author(s): Monideepa Roy, Pushpendu Kar, and Sujoy Datta
Series: Intelligent Systems
Publisher: CRC Press
Year: 2023
Language: English
Pages: 278
Cover
Half Title
Series
Title
Copyright
Contents
About the Editors
List of Contributors
Foreword
Preface
Chapter 1 Comparison of Different Machine Learning Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach
Chapter 2 An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies
Chapter 3 Neural Network-Based Collaborative Filtering for Recommender Systems
Chapter 4 Recommendation System and Big Data: Its Types and Applications
Chapter 5 The Role of Machine Learning/AI in Recommender Systems
Chapter 6 A Recommender System Based on TensorFlow Framework
Chapter 7 A Marketing Approach to Recommender Systems
Chapter 8 Applied Statistical Analysis in Recommendation Systems
Chapter 9 An IoT-Enabled Innovative Smart Parking Recommender Approach
Chapter 10 Classification of Road Segments in Intelligent Traffic Management System
Chapter 11 Facial Gestures-Based Recommender System for Evaluating Online Classes
Chapter 12 Application of Swarm Intelligence in Recommender Systems
Chapter 13 Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems
Chapter 14 Recommendation Systems for Choosing Online Learning Resources: A Hands-On Approach
Index
