Recommender Systems (eBook, ePUB)

A Multi-Disciplinary Approach
Redaktion: Roy, Monideepa; Datta, Sujoy; Kar, Pushpendu

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 278
• Erscheinungstermin: 19. Juni 2023
• Englisch
• ISBN-13: 9781000886283
• Artikelnr.: 67838687

Autorenporträt

Monideepa Roy, Pushpendu Kar, Sujoy Datta

Inhaltsangabe

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