Recommender Systems: A Multi-Disciplinary Approach

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Monideepa Roy (KIIT Deemed to be University, India) Pushpendu Kar (University of Nottingham, Ningbo China)

Recommender Systems

A Multi-Disciplinary Approach

Monideepa Roy (KIIT Deemed to be University, India), Pushpendu Kar (University of Nottingham, Ningbo China), Sujoy Datta (KIIT Deemed to be University, India)

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English

CRC Press
19 December 2024

Algorithms & data structures; Software Engineering; Databases; Artificial intelligence

Series: Intelligent Systems

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.

Edited by: Monideepa Roy (KIIT Deemed to be University India), Pushpendu Kar (University of Nottingham, Ningbo China), Sujoy Datta (KIIT Deemed to be University, India)
Imprint: CRC Press
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
Weight: 453g
ISBN: 9781032333229
ISBN 10: 1032333227
Series: Intelligent Systems
Pages: 260
Publication Date: 19 December 2024
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Comparison of Different Machine Learning Algorithms to Classify Whether or Not a Tweet Is about a Natural Disaster: A Simulation-Based Approach; 2. An End-to-End Comparison among Contemporary Content-Based Recommendation Methodologies; 3. Neural Network-Based Collaborative Filtering for Recommender Systems; 4. Recommendation System and Big Data: Its Types and Applications; 5. The Role of Machine Learning /AI in Recommender Systems; 6. A Recommender System Based on TensorFlow Framework; 7. A Marketing Approach to Recommender Systems; 8. Applied Statistical Analysis in Recommendation Systems; 9. An IoT-Enabled Innovative Smart Parking Recommender Approach; 10. Classification of Road Segments in Intelligent Traffic Management System; 11. Facial Gestures-Based Recommender System for Evaluating Online Classes; 12. Application of Swarm Intelligence in Recommender Systems; 13. Application of Machine-Learning Techniques in the Development of Neighbourhood-Based Robust Recommender Systems; 14. Recommendation Systems for Choosing Online Learning Resources: A Hands-On Approach

Monideepa Roy, Pushpendu Kar, Sujoy Datta